Can preoperative planning using IRIS™three-dimensional anatomical virtual models predict operative findings during robot-assisted partial nephrectomy?

Objective To evaluate the predictive validity of IRIS™(Intuitive Surgical®,Sunnyvale,CA,USA)as a planning tool for robot-assisted partial nephrectomy(RAPN)by assessing the degree of overlap with intraoperative execution.Methods Thirty-one patients scheduled for RAPN by four experienced urologists were enrolled in a prospective study.Prior to surgery,urologists reviewed the IRIS™three-dimensional model on an iphone Operating System(iOS)app and completed a questionnaire outlining their surgical plan including surgical approach,and ischemia technique as well as confidence in executing this plan.Postoperatively,questionnaires assessing the procedural approach,clinical utility,efficiency,and effectiveness of IRIS™were completed.The degree of overlap between the preoperative and intraoperative questionnaires and between the planned approach and actual execution of the procedure was analyzed.Questionnaires were answered on a 5-point Likert scale and scores of 4 or greater were considered positive.Results Mean age was 65.1 years with a mean tumor size of 27.7 mm(interquartile range 17.5-44.0 mm).Hilar tumors consisted of 32.3%;48.4%of patients had R.E.N.A.L.nephrometry scores of 7-9.On preoperative questionnaires,the surgeons reported that in 67.7%cases they were confident that they can perform the procedure successfully,and on intraoperative questionnaires,the surgeons reported that in 96.8%cases IRIS™helped achieve good spatial sensation of the anatomy.There was a high degree of overlap between preoperative and intraoperative questionnaires for the surgical approach,interpreting anatomical details and clinical utility.When comparing plans for selective or off-clamp,the preoperative plan was executed in 90.0%of cases intraoperatively.Conclusion A high degree of overlap between the preoperative surgical approach and intraoperative RAPN execution was found using IRIS™.This is the first study to evaluate the predictive accuracy of IRIS™during RAPN by comparing preoperative plan and intraoperative execution.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Currents and Electric Fields Induced in Anatomically Realistic Human Models by Extremely Low Frequency Electric Fields

There is increasing public concern about biological interactions with and the potential health effects of low frequency electric and magnetic fields. Recently, the ICNIRP （International Commission on Non-Ionizing Radiation Protection） has published new exposure guidelines with regard to these fields. The aim of this paper is to demonstrate the calculation of the currents and electric fields induced in the human body by external electric fields at 60 Hz, using numerical human models of anatomically-realistic human bodies, and to compare those results with the basic restrictions proposed by the new guidelines. As a result, in the case that a human is exposed to an electric field of 1 kV/m at 60 Hz the short-circuit current of 18 μA flows though the ankles. Furthermore, the electric field of 40 mV/m in the nervous tissue of the adult model is induced by exposure to external electric fields at the reference level, which is enough smaller than the basic restrictions established in the ICNIRP guidelines for occupational exposure.

Analysis of the inflection points of height-diameter models

The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.

Progress in experimental models to investigate the in vivo and in vitro antidiabetic activity of drugs

Diabetes mellitus is one of the world's most prevalent and complex metabolic disorders,and it is a rapidly growing global public health issue.It is characterized by hyperglycemia,a condition involving a high blood glucose level brought on by deficiencies in insulin secretion,decreased activity of insulin,or both.Prolonged effects of diabetes include cardiovascular problems,retinopathy,neuropathy,nephropathy,and vascular alterations in both macro-and micro-blood vessels.In vivo and in vitro models have always been important for investigating and characterizing disease pathogenesis,identifying targets,and reviewing novel treatment options and medications.Fully understanding these models is crucial for the researchers so this review summarizes the different experimental in vivo and in vitro model options used to study diabetes and its consequences.The most popular in vivo studies involves the small animal models,such as rodent models,chemically induced diabetogens like streptozotocin and alloxan,and the possibility of deleting or overexpressing a specific gene by knockout and transgenic technologies on these animals.Other models include virally induced models,diet/nutrition induced diabetic animals,surgically induced models or pancreatectomy models,and non-obese models.Large animals or non-rodent models like porcine(pig),canine(dog),nonhuman primate,and Zebrafish models are also outlined.The in vitro models discussed are murine and human beta-cell lines and pancreatic islets,human stem cells,and organoid cultures.The other enzymatic in vitro tests to assess diabetes include assay of amylase inhibition and inhibition ofα-glucosidase activity.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Effects of mesenchymal stem cell on dopaminergic neurons,motor and memory functions in animal models of Parkinson's disease:a systematic review and meta-analysis

Parkinson’s disease is chara cterized by the loss of dopaminergic neurons in the substantia nigra pars com pacta,and although restoring striatal dopamine levels may improve symptoms,no treatment can cure or reve rse the disease itself.Stem cell therapy has a regenerative effect and is being actively studied as a candidate for the treatment of Parkinson’s disease.Mesenchymal stem cells are considered a promising option due to fewer ethical concerns,a lower risk of immune rejection,and a lower risk of teratogenicity.We performed a meta-analysis to evaluate the therapeutic effects of mesenchymal stem cells and their derivatives on motor function,memory,and preservation of dopamine rgic neurons in a Parkinson’s disease animal model.We searched bibliographic databases(PubMed/MEDLINE,Embase,CENTRAL,Scopus,and Web of Science)to identify articles and included only pee r-reviewed in vivo interve ntional animal studies published in any language through J une 28,2023.The study utilized the random-effect model to estimate the 95%confidence intervals(CI)of the standard mean differences(SMD)between the treatment and control groups.We use the systematic review center for laboratory animal expe rimentation’s risk of bias tool and the collaborative approach to meta-analysis and review of animal studies checklist for study quality assessment.A total of 33studies with data from 840 Parkinson’s disease model animals were included in the meta-analysis.Treatment with mesenchymal stem cells significantly improved motor function as assessed by the amphetamine-induced rotational test.Among the stem cell types,the bone marrow MSCs with neurotrophic factor group showed la rgest effect size(SMD[95%CI]=-6.21[-9.50 to-2.93],P=0.0001,I^(2)=0.0%).The stem cell treatment group had significantly more tyrosine hydroxylase positive dopamine rgic neurons in the striatum([95%CI]=1.04[0.59 to 1.49],P=0.0001,I^(2)=65.1%)and substantia nigra(SMD[95%CI]=1.38[0.89 to 1.87],P=0.0001,I^(2)=75.3%),indicating a protective effect on dopaminergic neurons.Subgroup analysis of the amphetamine-induced rotation test showed a significant reduction only in the intracranial-striatum route(SMD[95%CI]=-2.59[-3.25 to-1.94],P=0.0001,I^(2)=74.4%).The memory test showed significant improvement only in the intravenous route(SMD[95%CI]=4.80[1.84 to 7.76],P=0.027,I^(2)=79.6%).Mesenchymal stem cells have been shown to positively impact motor function and memory function and protect dopaminergic neurons in preclinical models of Parkinson’s disease.Further research is required to determine the optimal stem cell types,modifications,transplanted cell numbe rs,and delivery methods for these protocols.

Immunobiology of COVID-19: Mechanistic and therapeutic insights from animal models

The distribution of the immune system throughout the body complicates in vitro assessments of coronavirus disease 2019(COVID-19)immunobiology,often resulting in a lack of reproducibility when extrapolated to the whole organism.Consequently,developing animal models is imperative for a comprehensive understanding of the pathology and immunology of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection.This review summarizes current progress related to COVID-19 animal models,including non-human primates(NHPs),mice,and hamsters,with a focus on their roles in exploring the mechanisms of immunopathology,immune protection,and long-term effects of SARS-CoV-2 infection,as well as their application in immunoprevention and immunotherapy of SARS-CoV-2 infection.Differences among these animal models and their specific applications are also highlighted,as no single model can fully encapsulate all aspects of COVID-19.To effectively address the challenges posed by COVID-19,it is essential to select appropriate animal models that can accurately replicate both fatal and non-fatal infections with varying courses and severities.Optimizing animal model libraries and associated research tools is key to resolving the global COVID-19 pandemic,serving as a robust resource for future emerging infectious diseases.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Knee osteoarthritis:A review of animal models and intervention of traditional Chinese medicine

Background:Knee osteoarthritis(KOA)characterized by degeneration of knee cartilage and subsequent bone hyperplasia is a prevalent joint condition primarily affecting aging adults.The pathophysiology of KOA remains poorly understood,as it involves complex mechanisms that result in the same outcome.Consequently,researchers are interested in studying KOA and require appropriate animal models for basic research.Chinese herbal compounds,which consist of multiple herbs with diverse pharmacological properties,possess characteristics such as multicomponent,multipathway,and multitarget effects.The potential benefits in the treatment of KOA continue to attract attention.Purpose:This study aims to provide a comprehensive overview of the advantages,limitations,and specific considerations in selecting different species and methods for KOA animal models.This will help researchers make informed decisions when choosing an animal model.Methods:Online academic databases(e.g.,PubMed,Google Scholar,Web of Science,and CNKI)were searched using the search terms“knee osteoarthritis,”“animal models,”“traditional Chinese medicine,”and their combinations,primarily including KOA studies published from 2010 to 2023.Results:Based on literature retrieval,this review provides a comprehensive overview of the methods of establishing KOA animal models;introduces the current status of advantages and disadvantages of various animal models,including mice,rats,rabbits,dogs,and sheep/goats;and presents the current status of methods used to establish KOA animal models.Conclusion:This study provides a review of the animal models used in recent KOA research,discusses the common modeling methods,and emphasizes the role of traditional Chinese medicine compounds in the treatment of KOA.

Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies

El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.

Neurophysiological, histological, and behavioral characterization of animal models of distraction spinal cord injury: a systematic review

Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.

High Prevalence of Anatomical Variations and Anomalies of the Coronary Arteries Detected by CT Angiography in Symptomatic Patients

Objective:Coronary artery anatomical variations and anomalies are an important topic due to their potential clinical manifestations.This study aims to investigate the prevalence of coronary artery anatomical variations and anomalies in symptomatic patients with coronary computed tomography angiography(CCTA).Methods:This is a retrospective study that included all symptomatic patients who had CCTA in a tertiary care hospital in Saudi Arabia during a period of seven years.Results:The total number of included patients was 507(60%males)with a mean age of 57.4 years.Approximately 41%had luminal stenoses,averaging 49.7%.The total num-ber of patients with coronary anatomical variations(CAV)and coronary artery anomalies(CAA)was 217(43%).CAV prevalence was 26%,which included 14%non-right coronary dominance,5%short left main coronary artery(LMCA),and 7%division variations(trifurcation and quadrifurcarion)of the LMCA.The prevalence of CAA was 29%,which included 5%origin anomalies,22%myocardial bridge,and 2%course anomalies.Conclusions:A high prevalence of coronary artery anatomic variations and anomalies in symptomatic patients is reported in this study.Systematic reviews,meta-analyses,reporting guidelines,and unified definitions and classifications of cor-onary variations and anomalies are lacking in the literature,presenting potential opportunities for future research and publications.

Theoretical investigation on axial cyclic performance of monopile in sands using interface constitutive models

Cyclic loads generated by environmental factors,such as winds,waves,and trains,will likely lead to performance degradation in pile foundations,resulting in issues like permanent displacement accumulation and bearing capacity attenuation.This paper presents a semi-analytical solution for predicting the axial cyclic behavior of piles in sands.The solution relies on two enhanced nonlinear load-transfer models considering stress-strain hysteresis and cyclic degradation in the pile-soil interaction.Model parameters are calibrated through cyclic shear tests of the sand-steel interface and laboratory geotechnical testing of sands.A novel aspect involves the meticulous formulation of the shaft loadtransfer function using an interface constitutive model,which inherently inherits the interface model’s advantages,such as capturing hysteresis,hardening,degradation,and particle breakage.The semi-analytical solution is computed numerically using the matrix displacement method,and the calculated values are validated through model tests performed on non-displacement and displacement piles in sands.The results demonstrate that the predicted values show excellent agreement with the measured values for both the static and cyclic responses of piles in sands.The displacement pile response,including factors such as bearing capacity,mobilized shaft resistance,and convergence rate of permanent settlement,exhibit improvements compared to non-displacement piles attributed to the soil squeezing effect.This methodology presents an innovative analytical framework,allowing for integrating cyclic interface models into the theoretical investigation of pile responses.

Evaluation of Functional and Radiological Outcome of Arthroscopic-Assisted Anatomical Coracoclavicular (CC) and Acromioclavicular (AC) Ligament Reconstruction in Chronic AC Joint Dislocation

Introduction: Acromioclavicular (AC) joint dislocation is a common shoulder injury, comprising 9% - 12% of shoulder girdle injuries. Optimal management remains challenging, with treatment decisions guided by the Rockwood classification system. Controversies surround grade III injuries, necessitating further classification. Non-operative treatment has shown favorable outcomes, while surgical interventions vary. Anatomical coracoclavicular reconstruction (ACCR) has demonstrated biomechanical advantages over traditional methods. Arthroscopic techniques offer advantages, minimizing deltoid detachment and allowing concurrent pathology identification. This study evaluates the outcomes of arthroscopic-assisted ACCR in chronic AC joint dislocation. Surgical Technique: Arthroscopic-assisted ACCR involves meticulous portal placement, tendon graft harvesting, diagnostic arthroscopy, and coracoid exposure. The clavicle tunnels were made to mimic the conoid and trapezoid ligament positions, using FibreTape#2 loop and Dog Bone Button for correct placement against the coracoid base, and passing the semitendinosus graft through to reconstruct the conoid ligament, reduction done and graft follow through for anatomical reconstruction. Methods: A retrospective cohort study at Hospital Kuala Lumpur analyzed 35 patients undergoing arthroscopic-assisted ACCR for Rockwood grade III - V AC joint dislocations. Inclusion criteria encompassed trauma ≥ 3 weeks prior, no prior shoulder injuries, and ≥12-month follow-up. Functional and radiological assessments utilized ASES scores and coracoclavicular distances, respectively. Statistical analysis employed descriptive statistics and logistic regression. Results: The mean age was 38.9 years (SD 11.26), and 34 of 35 patients were male. Grade IV injuries were predominant (37.1%). Waiting time for surgery averaged 234.9 days. Functional improvement was substantial postoperatively (ASES: 55.5 to 88.9). Radiological outcomes demonstrated reduced coracoclavicular distances and maintained reduction. No significant correlation was observed between injury grade and outcomes. Conclusion: Arthroscopic-assisted ACCR for chronic AC joint dislocation yields significant functional and radiological improvement, irrespective of injury grade. Waiting time for surgery exhibits minor impact on outcomes, emphasizing the procedure’s efficacy. Concomitant injuries do not impede success, highlighting the versatility of this approach in managing shoulder instability. The study contributes valuable insights into the nuanced management of chronic AC joint dislocations and supports the adoption of arthroscopic-assisted ACCR as a viable treatment option.

Spatiotemporal Evaluation and Future Projection of Diurnal Temperature Range over the Tibetan Plateau in CMIP6 Models

The diurnal temperature range(DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multitemporal DTR variations over the Tibetan Plateau. It assesses 23 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6) using CN05.1 observational data as validation, evaluating their ability to simulate DTR over the Tibetan Plateau. Then, the evolution of DTR over the Tibetan Plateau under different shared socioeconomic pathway(SSP) scenarios for the near,middle, and long term of future projection are analyzed using 11 selected robustly performing models. Key findings reveal:(1) Among the models examined, BCC-CSM2-MR, EC-Earth3, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR,FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and INM-CM5-0 exhibit superior integrated simulation capability for capturing the spatiotemporal variability of DTR over the Tibetan Plateau.(2) Projection indicates a slightly increasing trend in DTR on the Tibetan Plateau in the SSP1-2.6 scenario, and decreasing trends in the SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios. In certain areas, such as the southeastern edge of the Tibetan Plateau, western hinterland of the Tibetan Plateau, southern Kunlun, and the Qaidam basins, the changes in DTR are relatively large.(3) Notably, the warming rate of maximum temperature under SSP2-4.5, SSP3-7.0, and SPP5-8.5 is slower compared to that of minimum temperature, and it emerges as the primary contributor to the projected decrease in DTR over the Tibetan Plateau in the future.