Efficacy of stromal vascular fraction for knee osteoarthritis:A prospective,single-centre,non-randomized study with 2 years follow-up

BACKGROUND Current osteoarthritis(OA)treatments focus on symptom relief without addressing the underlying disease process.In regenerative medicine,current treatments have limitations.In regenerative medicine,more research is needed for intra-articular stromal vascular fraction(SVF)injections in OA,including dosage optimization,long-term efficacy,safety,comparisons with other treatments,and mechanism exploration.AIM To compare the efficacy of intra-articular SVF with corticosteroid(ICS)injections in patients with primary knee OA.METHODS The study included 50 patients with Kellgren-Lawrence grades II and III OA.Patients were randomly assigned(1:1)to receive either a single intra-articular SVF injection(group A)or a single intra-articular ICS(triamcinolone)(group B)injection.Patients were followed up at 1,3,6,12,and 24 months.Visual analog score(VAS)and International Knee Documentation Committee(IKDC)scores were administered before the procedure and at all followups.The safety of SVF in terms of adverse and severe adverse events was recorded.Statistical analysis was performed with SPSS Version 26.0,IBM Corp,Chicago,IL,United States.RESULTS Both groups had similar demographics and baseline clinical characteristics.Follow-up showed minor patient loss,resulting in 23 and 24 in groups A and B respectively.Group A experienced a notable reduction in pain,with VAS scores decreasing from 7.7 to 2.4 over 24 months,compared to a minor reduction from 7.8 to 6.2 in Group B.This difference in pain reduction in group A was statistically significant from the third month onwards.Additionally,Group A showed significant improvements in knee functionality,with IKDC scores rising from 33.4 to 83.10,whereas Group B saw a modest increase from 36.7 to 45.16.The improvement in Group A was statistically significant from 6 months and maintained through 24 months.CONCLUSION Our study demonstrated that intra-articular administration of SVF can lead to reduced pain and improved knee function in patients with primary knee OA.More adequately powered,multi-center,double-blinded,randomised clinical trials with longer follow-ups are needed to further establish safety and justify its clinical use.

Autologous peripheral blood-derived orthobiologics:Different types and their effectiveness in managing knee osteoarthritis

Knees are the most commonly impacted weight-bearing joints in osteoarthritis(OA),affecting millions of people worldwide.With increasing life spans and obesity rates,the incidence of knee OA will further increase,leading to a significant increase in the economic burden.Conventional treatment modalities utilized to manage knee OA have limitations.Over the last decade,the role of various autologous peripheral blood-derived orthobiologics(APBOs)for the treatment of knee OA has been extensively investigated.This editorial provided an overview and focused on defining and shedding light on the current state of evidence based on the most recent published clinical studies concerning the use of APBO for the management of knee OA.While numerous studies have demonstrated promising results for these preparations,a notable gap exists in the comparative analysis of these diverse formulations.This absence of head-to-head studies poses a considerable challenge for physicians/surgeons in determining the optimal preparation for managing knee OA and achieving sustained longterm results.Thus,more adequately powered,multicenter,prospective,doubleblind,randomized controlled trials with longer follow-ups are needed to establish the long-term efficacy and to aid physicians/surgeons in determining the optimal APBO for the management of knee OA.

Effect of weight-adjusted antimicrobial antibiotic prophylaxis on postoperative dosage and surgical site infection incidence in total joint arthroplasty

Surgical site infections(SSI)following total joint arthroplasty pose a significant concern for both providers and patients across the globe.Currently,administration of antimicrobial antibiotic prophylaxis is used throughout the world to reduce the incidence of SSI.However,the correct dosage and frequency of administration remains debatable.In this editorial,we emphasized the determination of the effect of administration of weight-adjusted antimicrobial antibiotic prophylaxis regime on the incidence of SSI and postoperative dosage reduction compared to the conventionally used regime during total joint arthroplasty.The results demonstrated similar efficacy between both regimes with respect to the incidence of SSI.In addition,weight-adjustment led to reduced postoperative dosage and has the potential to reduce chances of achieving lower therapeutic concentration,drug resistance,drug toxicity,and costs.

Choosing ankle tourniquets in foot and ankle surgery: Beyond postoperative pain considerations

This editorial critically explores the use of ankle vs thigh tourniquets in foot and ankle surgery based on a recent study that found no significant difference in postoperative pain between the two placement techniques.Despite these findings,we argue for the preferential use of ankle tourniquets,highlighting their potential benefits in reducing venous blood stasis and minimizing soft tissue injury.This approach underscores the importance of considering long-term patient outcomes and vascular health beyond immediate postoperative pain.By integrating study findings with broader clinical considerations,we hereby advocate for a nuanced approach to tourniquet use that prioritizes patient safety and long-term recovery in conjunction with immediate postoperative pain.

The First Pilot Epigenetic Type Improvement of Neuropsychiatric Symptoms in a Polymorphic Dopamine D2 (-DRD2/ANKK (Taq1A)), OPRM1 (A/G), DRD3 (C/T), and MAOA (4R) Compromised Preadolescence Male with Putative PANDAS/CANS: Positive Clinical Outcome with Precision-Guided DNA Testing and Pro-Dopamine Regulation (KB220) and Antibacterial Therapies

Pediatric autoimmune neuropsychiatric disorders associated with or without streptococcal and other bacterial infections (PANDAS/CANS) are emerging as a featured pediatric disorder. Although there is some controversy regarding treatment approaches, especially related to the behavioral sequelae, we have hypothesized in other published work that it is characterized by the rapid onset of Reward Deficiency Syndrome (RDS) in children. We propose utilizing a multi-systems biological approach involving the coupling of genetic addiction risk testing and pro-dopamine regulation (KB220/POLYGEN®) to help induce “dopamine homeostasis” in patients with PANDAS, especially those with known DNA-induced hypodopaminergia. This case study examines a 12-year-old Caucasian male with no prior psychiatric issues who presented with a sudden onset of severe anxiety, depression, emotional liability, and suicidal ideation. The patient underwent genotyping and the genetic addiction risk score (GARS) testing, which revealed risk polymorphisms in the dopamine D2 (-DRD2/ANKK (Taq1A), OPRM1 (A/G), DRD3 (C/T), and MAOA (4R) genes. These polymorphisms have been linked to hypodopaminergia. The patient was subsequently placed on research ID-KB220ZPBMPOLY (POLYGEN®), and albeit the possibility of bias, based upon self and parental assessment, a marked rapid improvement in psychiatric symptoms was observed. In the second phase of treatment (102 days utilizing KB220), the patient received standard antibody testing, which was positive for Lyme. Antibacterial therapy started immediately, and KB220z was discontinued to provide a wash-out period. A monotonic trend analysis was performed on each outcome measure, and a consistently decreasing trend was observed utilizing antibacterial therapy. Our recommendation, albeit only one case, is to utilize and further research a combined therapeutic approach, involving precision-guided DNA testing and pro-dopamine regulation along with antibacterial therapy, as well as glutathione to address offensive enhanced cytokines, in patients with suspected PANDAS/CANS.

Silver nanoparticle technology in orthopaedic infections

The irrational and prolonged use of antibiotics in orthopaedic infections poses a major threat to the development of antimicrobial resistance.To combat antimi-crobial resistance,researchers have implemented various novel and innovative modalities to curb infections.Nanotechnology involves doping ions/metals onto the scaffolds to reach the target site to eradicate the infective foci.In this conno-tation,we reviewed silver nanoparticle technology in terms of mechanism of action,clinical applications,toxicity,and regulatory guidelines to treat ortho-paedic infections.

Machine learning and deep neural network-based learning in osteoarthritis knee

Osteoarthritis(OA)of the knee joint is considered the commonest musculoskeletal condition leading to marked disability for patients residing in various regions around the globe.Application of machine learning(ML)in doing research regarding OA has brought about various clinical advances viz,OA being diagnosed at preliminary stages,prediction of chances of development of OA among the population,discovering various phenotypes of OA,calculating the severity in OA structure and also discovering people with slow and fast progression of disease pathology,etc.Various publications are available regarding machine learning methods for the early detection of osteoarthritis.The key features are detected by morphology,molecular architecture,and electrical and mechanical functions.In addition,this particular technique was utilized to assess non-interfering,non-ionizing,and in-vivo techniques using magnetic resonance imaging.ML is being utilized in OA,chiefly with the formulation of large cohorts viz,the OA Initiative,a cohort observational study,the Multicentre Osteoarthritis Study,an observational,prospective longitudinal study and the Cohort Hip&Cohort Knee,an observational cohort prospective study of both hip and knee OA.Though ML has various contributions and enhancing applications,it remains an imminent field with high potential,also with its limitations.Many more studies are to be carried out to find more about the link between machine learning and knee osteoarthritis,which would help in the improvement of making decisions clinically,and expedite the necessary interventions.

Gap models across micro-to mega-scales of time and space:examples of Tansley’s ecosystem concept

Background:Gap models are individual-based models for forests.They simulate dynamic multispecies assemblages over multiple tree-generations and predict forest responses to altered environmental conditions.Their development emphases designation of the significant biological and ecological processes at appropriate time/space scales.Conceptually,they are with consistent with A.G.Tansley’s original definition of"the ecosystem".Results:An example microscale application inspects feedbacks among terrestrial vegetation change,air-quality changes from the vegetation’s release of volatile organic compounds(VOC),and climate change effects on ecosystem production of VOC’s.Gap models can allocate canopy photosynthate to the individual trees whose leaves form the vertical leaf-area profiles.VOC release depends strongly on leaf physiology by species of these trees.Leaf-level VOC emissions increase with climate-warming.Species composition change lowers the abundance of VOC-emitting taxa.In interactions among ecosystem functions and biosphere/atmosphere exchanges,community composition responses can outweigh physiological responses.This contradicts previous studies that emphasize the warming-induced impacts on leaf function.As a mesoscale example,the changes in climate(warming)on forests including pest-insect dynamics demonstrates changes on the both the tree and the insect populations.This is but one of many cases that involve using a gap model to simulate changes in spatial units typical of sampling plots and scaling these to landscape and regional levels.As this is the typical application scale for gap models,other examples are identified.The insect/climatechange can be scaled to regional consequences by simulating survey plots across a continental or subcontinental zone.Forest inventories at these scales are often conducted using independent survey plots distributed across a region.Model construction that mimics this sample design avoids the difficulties in modelling spatial interactions,but we also discuss simulation at these scales with contagion effects.Conclusions:At the global-scale,successful simulations to date have used functional types of plants,rather than tree species.In a final application,the fine-scale predictions of a gap model are compared with data from micrometeorological eddy-covariance towers and then scaled-up to produce maps of global patterns of evapotranspiration,net primary production,gross primary production and respiration.New active-remote-sensing instruments provide opportunities to test these global predictions.

Hexanuclear ring cobalt complex for photochemical CO_(2) to CO conversion

Photosynthesis in nature has been deemed as the most significant biochemical reaction,which maintains a relatively stable content of O_(2) and CO_(2) in the atmosphere.Herein,for a deeper comprehension of natural photosynthesis,an artificial photosynthesis model reaction of photochemical CO_(2) to CO conversion(CO_(2)+2 H^(+)+2e^(-)→CO+H_(2)O)catalyzed by a homogeneous hexanuclear ring cobalt complex{K_(2)[CoO_(3)PCH_(2)N(CH_(2)CO_(2))_(2)]}_(6)(Co6 complex)is developed.Using the[Ru(bpy)_(3)]^(2+)as a photosensitizer and TEOA as a sacrificial electron donor,an optimal turnover frequency of 503.3 h^(‒1) and an apparent quantum efficiency of 0.81%are obtained.The good photocatalytic CO_(2) reduction performance is attributed to the efficient electron transfer between Co6 complex and[Ru(bpy)_(3)]^(2+),which boosts the photogenerated carriers separation of the photosensitizer.It is confirmed by the j‐V curves,light‐assisted UV‐vis curves,steady‐state photoluminescence spectra and real‐time laser flash photolysis experiments.In addition,the proposed catalytic mechanism for CO_(2) reduction reaction catalyzed by the Co6 complex is explored by the potassium thiocyanate poison experiment,Pourbaix diagram and density functional theory calculations.

Molecular Docking of Selective Binding Affinity of Sulfonamide Derivatives as Potential Antimalarial Agents Targeting the Glycolytic Enzymes: GAPDH, Aldolase and TPI

The parasite Plasmodium falciparum is responsible for the major world scourge malaria, a disease that affects 3.3 billion people worldwide. The development of new drugs is critical because of the diminished effectiveness of current antimalarial agents mainly due to parasitic resistance, side effects and cost. Molecular docking was used to explore structural motifs responsible for the interactions between triose phosphate isomerase (TPI), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and aldolase (ALD) from human and Plasmodium cells with 8 novel sufonylamide derivatives. All the ligands modeled, interact with all three enzymes in the micromolar range. The top ligand (sulfaE) shows a 70-fold increase in selective binding to pfTPI compared to hTPI (dissociation constant-KI of 7.83 μM and 0.177 μM for hTPI and pfTPI respectively), on par with antimalarial drug chloroquine.ALD and GAPDH form complexes with similar binding sites, comprising amino acids of similar chemical properties and polarities. Human TPI and pfTPI bind sulfonamide derivatives using two distinct binding sites and residues. Key residues at the dimer interface of pfTPI (VAL44, SER45, TYR48, GLN64, ASN65, VAL78) form a tight pocket with favorable polar contacts. The affinity with TPI is the most specific, stable, and selective suggesting pfTPI is a candidate for development of antimalarial drugs.

Preoperative prediction of lymph node metastasis using deep learning-based features

Lymph node involvement increases the risk of breast cancer recurrence.An accurate non-invasive assessment of nodal involvement is valuable in cancer staging,surgical risk,and cost savings.Radiomics has been proposed to pre-operatively predict sentinel lymph node(SLN)status;however,radiomic models are known to be sensitive to acquisition parameters.The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based(DLB)features and compare its predictive performance to state-of-the-art radiomics.Specifically,this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution.Dynamic contrast-enhancement images from 198 patients(67 positive SLNs)were used in this study.Of these subjects,163 had an in-plane resolution of 0.7×0.7 mm^(2),which were randomly divided into a training set(approximately 67%)and a validation set(approximately 33%).The remaining 35 subjects with a different in-plane resolution(0.78×0.78 mm^(2))were treated as independent testing set for generalizability.Two methods were employed:(1)conventional radiomics(CR),and(2)DLB features which replaced hand-curated features with pre-trained VGG-16 features.The threshold determined using the training set was applied to the independent validation and testing dataset.Same feature reduction,feature selection,model creation procedures were used for both approaches.In the validation set(same resolution as training),the DLB model outperformed the CR model(accuracy 83%vs 80%).Furthermore,in the independent testing set of the dissimilar resolution,the DLB model performed markedly better than the CR model(accuracy 77%vs 71%).The predictive performance of the DLB model outperformed the CR model for this task.More interestingly,these improvements were seen particularly in the independent testing set of dissimilar resolution.This could indicate that DLB features can ultimately result in a more generalizable model.

The Cellulolytic Bacteria <i>R. albus</i>for Improving the Efficiency of Microbial Fuel Cell

The current study has been undertaken to examine the beneficial effect in the power output of a microbial fuel cell (MFC) by adding cellulolytic bacteria Ruminococcus albus (R. albus) into the anodic chamber. Mediator-less H-type MFCs were set up where the anode chamber contained anaerobic digester microorganisms as inocula on finely ground pine tree (Avicel) at 2% (w/v) and the cathode chamber of 10mM phosphate buffered saline conductive solution, both separated by a cation exchange membrane. The functioning of the MFCs for generation of electrical power and the amounts of gaseous byproducts was monitored over a 9-day period. The addition of cellulolytic bacteria caused an increase of average power density from 7.9 m W/m2 to19.5 m W/m2, about 245% increase over a 9-day period. For both groups of MFCs;with R. albus and the control, the head space gases collected were methane and CO2. While the methane: CO2 ratios were found unchanged at 1.7:1 throughout the 9 days of operation, the total gas production increased from 248 mL to 319 mL due to the presence of R. albus addition. This study confirms that whereas the biocatalytic activity of anode microbial population determines the energy production, the addition of external cellulolytic bacteria into anode microbial population can improve and extend the biomass utilization.

Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Presence of higher breast density(BD)and persistence over time are risk factors for breast cancer.A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable.In this study,we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures.Three datasets of volunteers from two clinical trials were included.Breast MR images were acquired on 3T Siemens Biograph mMR,Prisma,and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique.Two whole-breast segmentation strategies,utiliz-ing image registration and 3D U-Net,were developed.Manual segmentation was performed.A task-based analysis was performed:a previously developed MR-based BD measure,MagDensity,was calculated and assessed using automated and manual segmentation.The mean squared error(MSE)and intraclass correlation coefficient(ICC)between MagDensity were evaluated using the manual segmentation as a reference.The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures(Δ_(2-1)),MSE,and ICC.The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation,with ICCs of 0.986(95%CI:0.974-0.993)and 0.983(95%CI:0.961-0.992),respectively.For test-retest analysis,MagDensity derived using the regis-tration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993(95%CI:0.982-0.997)when compared to other segmentation methods.In conclusion,the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD.Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment,with the registration exhibiting superior performance for highly reproducible BD measurements.

Neuromelanin,one of the most overlooked molecules in modern medicine,is not a spectator

The loss of pigmented neurons from the human brain has long been the hallmark of Parkinson＇s disease（PD）.Neuromelanin（NM） in the pre-synaptic terminal of dopamine neurons is emerging as a primary player in the etiology of neurodegenerative disorders including PD.This mini-review discusses the interactions between neuromelanin and different molecules in the synaptic terminal and describes how these interactions might affect neurodegenerative disorders including PD.Neuromelanin can reversibly bind and interact with amine containing neurotoxins,e.g.,MPTP,to augment their actions in the terminal,eventually leading to the instability and degeneration of melanin-containing neurons due to oxidative stress and mitochondrial dysfunction.In particular,neuromelanin appears to confer susceptibility to chemical toxicity by providing a large sink of iron-bound,heme-like structures in a pi-conjugated system,a system seemingly purposed to allow for stabilizing interactions including pi-stacking as well as ligand binding to iron.Given the progressive accumulation of NM with age corresponding with an apparent decrease in dopamine synthetic pathways,the immediate question of whether NM is also capable of binding dopamine,the primary functional monoamine utilized in this cell,should be raised.Despite the rather glaring implications of this finding,this idea appears not to have been adequately addressed.As such,we postulate on potential mechanisms by which dopamine might dissociate from neuromelanin and the implications of such a reversible relationship.Intriguingly,if neuromelanin is able to sequester and release dopamine in membrane bound vesicles,this intracellular pre-synaptic mechanism could be the basis for a form of chemical memory in dopamine neurons.

Degradation Rates of Native versus Exotic Leaves in a Tributary of the Yellow River in Georgia

Forested aquatic streams depend heavily on forest canopy input. This input is a primary resource for the macroinvertebrate fauna. As a result, changes in the canopy impact the aquatic ecosystem. The focus of this study was to identify leaf degradation rates to determine resource availability for invertebrate communities. Specifically, leaf degradation rates were determined for oak, poplar, maple and kudzu. Oak, poplar, and maple are established stream canopy vegetation while kudzu is an invasive species. By comparing leaf degradation rates of native vs. exotic leaves, it provides an insight to changes in community structure. Furthermore, these changes in the plant canopy biodiversity have long-term implications for stream health.

American Senior Communities: Healthcare Fraud Detection

Healthcare fraud is an increasingly large problem in the United States for patients, taxpayers, and the government, with the National Healthcare Anti-Fraud Association (NHCAA) estimating the costs to be more than tens of billions each year (NHCAA, 2018). To address this issue, government agencies and insurers can utilize data analytics to detect and prevent healthcare fraud. The American Senior Communities (ASC) case is a recent example of a complex healthcare fraud scheme committed by several high ranking officers involving kickbacks, fictitious vendors, and money laundering through shell companies. The indictment details how $16 million was stolen is particularly given the population cared for by ASC—the elderly, individuals with disabilities, low income adults, pregnant women and children. This case demonstrates several ways healthcare fraud can be perpetrated, highlights the role of the auditor, and introduces students to the importance of employing data analytics to prevent and detect fraud.

Mitochondrial Haplotype Analysis of <i>Pomoxis nigromaculatus</i>Inhabiting Three Georgian Lakes

Pomoxis nigromaculatus, more commonly referred to as black crappie is indigenous to fresh water streams and lakes in the eastern United States and supports an important recreational fishery. We examined the genetic population structure of black crappie inhabiting three Georgian Lakes, Lake Sidney Lanier, Lake Seminole and Hartwell Lake. DNA sequencing of 229 fish samples, utilizing the DNA barcode marker cytochrome oxidase subunit I (COI) revealed 27 polymorphic sites which defined nine haplotypes. Only haplotype 2 was shared between all sample sites with six other haplotypes being unique for individual lakes, for an overall haplotype diversity of 0.734. Tajima’s D and Fu’s tests were implemented to assess departures from neutral expectations. Fst pairwise comparisons were statistically significant among all populations of black crappie evaluated in this study.

＂Greasing the Skids＂： Interdisciplinary Rhetoric and Mindblindness

The idea of mindblindness reaching outside of neuroscience is an important one. It is significant because there is concern in all quarters about the prevalence and meaning of autism diagnoses. Secondly, mindblindness rhetoric reflects the kinds of rhetorical devices scholars use to analyze this theory. Finally, mindblindness is a fertile ground for research collaboration between neuroscientists, social scientists and humanities scholars as it skirts the boundaries of disciplinarity. What about mindblindness theory makes it an interdisciplinary phenomenon, complete with interdisciplinary collaborations and mutual knowledge-seeking？ I argue in this paper that specific forms of rhetoric ＂grease the skids,＂ meaning that they can be construed flexibly in both the neuroscientific language and the ＂other-discipline＂ language. Because of the flexibility of these rhetorical conveyances, interdisciplinary collaboration has exploded around mindblindness dialogue, despite the traditional differences in disciplinary methodology.

The Application of Cluster Analysis in Type II Diabetes Genome Association Study

Genetic diseases, such as Type II diabetes, are caused by a combination of environmental factors and mutations in multiple genes. Patients who have been diagnosed with such diseases cannot easily be treated. However, many diseases can be avoided if people at high risk change their living style, one example is their diet. Genome association study has been used to identify the risk factor of genetic disease. With the development of DNA microarray technique, it is possible to access the human genetic information related to specific diseases. This paper uses a combinatorial method to analyze the genetic case-control data for Type II diabetes. A distance based cluster method has been applied to publicly available genotype data on Type II diabetes for epidemiological study and achieved a high accurate result.

Distributed Least-Squares Iterative Methods in Large-Scale Networks:A Survey

Many science and engineering applications involve solvinga linear least-squares system formed from some field measurements. In the distributed cyber-physical systems(CPS),each sensor node used for measurement often only knowspartial independent rows of the least-squares system. To solve the least-squares all the measurements must be gathered at a centralized location and then perform the computa-tion. Such data collection and computation are inefficient because of bandwidth and time constraints and sometimes areinfeasible because of data privacy concerns. Iterative methods are natural candidates for solving the aforementionedproblem and there are many studies regarding this. However,most of the proposed solutions are related to centralized/parallel computations while only a few have the potential to beapplied in distributed networks. Thus distributed computations are strongly preferred or demanded in many of the realworld applications, e.g. smart-grid, target tracking, etc. Thispaper surveys the representative iterative methods for distributed least-squares in networks.