Application of Time-Tracking Platform in the Reperfusion Treatment of Patients with Acute Ischemic Stroke in Primary Hospitals

Objective To explore the application effect of time tracking platform in improving the reperfusion treatment of patients with acute ischemic stroke in primary hospitals. Methods and Results Patients with acute ischemic stroke who carried out emergency intravenous thrombolysis and arterial thrombectomy in our hospital in 2021, 2022 and 2023 were selected. The time tracking mode was implemented, and the patients were recorded at each time node of the hospital and the whole-process digital management was conducted. Compared the mean DNT (Door to Needle Time) of intravenous thrombolysis in emergency stroke patients in 2021, 2022 and 2023, the total number of hospital cases within 4.5 h of onset, the total number of thrombolysis cases within 4.5 h of onset, the number of intravenous thrombolysis in 60 minutes of acute ischemic stroke, and the number of thrombolysis cases. The results show that from 2021 to 2023 our emergency stroke patients with intravenous thrombolysis average DNT shortened year by year, to the hospital within 4.5 h after the onset of the difference is statistically significant (all P < 0.05) conclusion through the application of stroke time tracking platform, is beneficial to shorten the treatment time of each link, can effectively reduce the hospital time delay, improve the rate of thrombolysis, improve the reperfusion of stroke centers in primary hospitals.

Construction and validation of somatic mutation-derived long noncoding RNAs signatures of genomic instability to predict prognosis of hepatocellular carcinoma

BACKGROUND Long non-coding RNAs(LncRNAs)have been found to be a potential prognostic factor for cancers,including hepatocellular carcinoma(HCC).Some LncRNAs have been confirmed as potential indicators to quantify genomic instability(GI).Nevertheless,GI-LncRNAs remain largely unexplored.This study established a GI-derived LncRNA signature(GILncSig)that can predict the prognosis of HCC patients.AIM To establish a GILncSig that can predict the prognosis of HCC patients.METHODS Identification of GI-LncRNAs was conducted by combining LncRNA expression and somatic mutation profiles.The GI-LncRNAs were then analyzed for functional enrichment.The GILncSig was established in the training set by Cox regression analysis,and its predictive ability was verified in the testing set and TCGA set.In addition,we explored the effects of the GILncSig and TP53 on prognosis.RESULTS A total of 88 GI-LncRNAs were found,and functional enrichment analysis showed that their functions were mainly involved in small molecule metabolism and GI.The GILncSig was constructed by 5 LncRNAs(miR210HG,AC016735.1,AC116351.1,AC010643.1,LUCAT1).In the training set,the prognosis of high-risk patients was significantly worse than that of low-risk patients,and similar results were verified in the testing set and TCGA set.Multivariate Cox regression analysis and stratified analysis confirmed that the GILncSig could be used as an independent prognostic factor.Receiver operating characteristic curve analysis of the GILncSig showed that the area under the curve(0.773)was higher than the two LncRNA signatures published recently.Furthermore,the GILncSig may have a better predictive performance than TP53 mutation status alone.CONCLUSION We established a GILncSig that can predict the prognosis of HCC patients,which will help to guide prognostic evaluation and treatment decisions.

Numerical analysis of downward progressive landslides in long natural slopes with sensitive clay

Landslides occurring in sensitive clay often result in widespread destruction,posing a significant risk to human lives and property due to the substantial decrease in undrained shear strength during deformation.Assessing the consequences of these landslides is challenging and necessitates robust numerical methods to comprehensively investigate their failure mechanisms.While studies have extensively explored upward progressive landslides in sensitive clays,understanding downward progressive cases remains limited.In this study,we utilised the nodal integration-based particle finite element method(NPFEM)with a nonlinear strain-softening model to analyse downward progressive landslides in sensitive clay on elongated slopes,induced by surcharge loads near the crest.We focused on elucidating the underlying failure mechanisms and evaluating the effects of different soil parameters and strainsoftening characteristics.The simulation results revealed the typical pattern for downward landslides,which typically start with a localised failure in proximity to the surcharge loads,followed by a combination of different types of failure mechanisms,including single flow slides,translational progressive landslides,progressive flow slides,and spread failures.Additionally,inclined shear bands occur within spread failures,often adopting distinctive ploughing patterns characterised by triangular shapes.The sensitive clay thickness at the base,the clay strength gradient,the sensitivity,and the softening rate significantly influence the failure mechanisms and the extent of diffused displacement.Remarkably,some of these effects mirror those observed in upward progressive landslides,underscoring the interconnectedness of these phenomena.This study contributes valuable insights into the complex dynamics of sensitive clay landslides,shedding light on the intricate interplay of factors governing their behaviour and progression.

Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam

Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was employed.Phi correlation coefficients assessed associations among pairs of long COVID symptoms.Additionally,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and adolescents.Results:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in adolescents.Common symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two doses.Besides,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven days.Conclusions:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.

Modeling injection-induced fault slip using long short-term memory networks

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

Lipid metabolism-related long noncoding RNAs:A potential prognostic biomarker for hepatocellular carcinoma

The incidence rates of hepatocellular carcinoma(HCC)have increased in recent decades.Despite advancements in therapy and early diagnosis improving shortterm prognosis,long-term outcomes remain poor.Long noncoding RNAs(lncRNAs)and lipid metabolism play crucial roles in the development and progression of HCC.Enhanced lipid synthesis promotes HCC progression,and lncRNAs can reprogram the expression of lipogenic enzymes.Consequently,lipid metabolism-related(LMR)-lncRNAs regulate lipid anabolism,accelerating the onset and progression of HCC.This suggests that LMR-lncRNAs could serve as novel prognostic biomarkers and therapeutic targets.

Failure mechanism and simulation for long run-out of the catastrophic rock landslide in the Shanyang Vanadium Mine,China

On 12th August 2015,a massive rapid long run-out rock landslide occurred in the Shanyang Vanadium Mine in Shaanxi Province,China,which claimed the lives of 65 miners.No heavy rainfalls,earthquakes,and mining blasts were recorded before the incident.Therefore,the failure mechanism and the cause of the long run-out movement are always in arguments.In this paper,we conducted a detailed field investigation,laboratory tests,block theory analysis,and numerical simulation to investigate the failure and long run-out mechanisms of the landslide.The field investigation results show that the source material of the rock landslide is a huge dolomite wedge block bedding on siliceous shale layers.Uniaxial compression tests indicate that the uniaxial compression strength of the intact dolomite is 130-140MPa and the dolomite shows a brittle failure mode.Due to the progressive downward erosion of the gully,the dolomite rock bridge at the slope toe became thinner.As the compression stress in the dolomite bridge increased to surpass its strength,the brittle failure of the bridge occurred.Then huge potential energy was released following the disintegration of the landslide,which led to the high acceleration of this rock landslide.The 3D discrete element simulation results suggest that the low intergranular friction contributes to the long run-out movement of this rock landslide.

Navigating the labyrinth of long non-coding RNAs in colorectal cancer:From chemoresistance to autophagy

Long non-coding RNAs(lncRNAs),with transcript lengths exceeding 200 nucleotides and little or no protein-coding capacity,have been found to impact colorectal cancer(CRC)through various biological processes.LncRNA expression can regulate autophagy,which plays dual roles in the initiation and progression of cancers,including CRC.Abnormal expression of lncRNAs is associated with the emergence of chemoresistance.Moreover,it has been confirmed that targeting autophagy through lncRNA regulation could be a viable approach for combating chemoresistance.Two recent studies titled“Human β-defensin-1 affects the mammalian target of rapamycin pathway and autophagy in colon cancer cells through long non-coding RNA TCONS_00014506”and“Upregulated lncRNA PRNT promotes progression and oxaliplatin resistance of colorectal cancer cells by regulating HIPK2 transcription”revealed novel insights into lncRNAs associated with autophagy and oxaliplatin resistance in CRC,respectively.In this editorial,we particularly focus on the regulatory role of lncRNAs in CRC-related autophagy and chemoresistance since the regulation of chemotherapeutic sensitivity by intervening with the lncRNAs involved in the autophagy process has become a promising new approach for cancer treatment.

High-modulus solid electrolyte interphase layer with gradient composition enables long-cycle all-solid-state lithium-sulfur batteries

All-solid-state lithium-sulfur batteries(ASSLSBs) have become one of the most potential candidates for the next-generation high-energy systems due to their intrinsic safety and high theoretical energy density.However, PEO-based ASSLSBs face the dilemma of insufficient Coulombic efficiency and long-term stability caused by the coupling problems of dendrite growth of anode and polysulfide shuttle of cathode. In this work, 1,3,5-trioxane(TOX) is used as a functional additive to design a PEO-based composite solidstate electrolyte(denoted as TOX-CSE), which realizes the stable long-term cycle of an ASSLSB. The results show that TOX can in-situ decompose on the anode to form a composite solid electrolyte interphase(SEI) layer with rich-organic component. It yields a high average modulus of 5.0 GPa, greatly improving the mechanical stability of the SEI layer and thus inhibiting the growth of dendrites. Also,the robust SEI layer can act as a barrier to block the side reaction between polysulfides and lithium metal.As a result, a Li-Li symmetric cell assembled with a TOX-CSE exhibits prolonged cycling stability over 2000 h at 0.2 m A cm^(-2). The ASSLSB also shows a stable cycling performance of 500 cycles at 0.5 C.This work reveals the structure–activity relationship between the mechanical property of interface layer and the battery's cycling stability.

Non-flammable long chain phosphate ester based electrolyte via competitive solventized structures for high-performance lithium metal batteries

Safety remains a persistent challenge for high-energy-density lithium metal batteries(LMBs).The development of safe and non-flammable electrolytes is especially important in harsh conditions such as high temperatures.Herein,a flame-retardant,low-cost and thermally stable long chain phosphate ester based(tributyl phosphate,TBP)electrolyte is reported,which can effectively enhance the cycling stability of highly loaded high-nickel LMBs with high safety through co-solvation strategy.The interfacial compatibility between TBP and electrode is effectively improved using a short-chain ether(glycol dimethyl ether,DME),and a specially competitive solvation structure is further constructed using lithium borate difluorooxalate(LiDFOB)to form the stable and inorganic-rich electrode interphases.Benefiting from the presence of the cathode electrolyte interphase(CEI)and solid electrolyte interphase(SEI)enriched with LiF and Li_(x)PO_(y)F_(z),the electrolyte demonstrates excellent cycling stability assembled using a 50μm lithium foil anode in combination with a high loading NMC811(15.4 mg cm^(-2))cathode,with 88%capacity retention after 120 cycles.Furthermore,the electrolyte exhibits excellent high-temperature characteristics when used in a 1-Ah pouch cell(N/P=0.26),and higher thermal runaway temperature(238℃)in the ARC(accelerating rate calorimeter)demonstrating high safety.This novel electrolyte adopts long-chain phosphate as the main solvent for the first time,and would provide a new idea for the development of extremely high safety and high-temperature electrolytes.

Long-term assessment of collagenase treatment for Dupuytren’s contracture:A 10-year follow-up study

BACKGROUND Enzymatic fasciotomy with collagenase clostridium histolyticum(CCH)has revolutionized the treatment for Dupuytren’s contracture(DC).Despite its benefits,the long-term outcomes remain unclear.This study presented a comprehensive 10-year follow-up assessment of the enduring effects of CCH on patients with DC.AIM To compare the short-term(12 wk)and long-term(10 years)outcomes on CCH treatment in patients with DC.METHODS A cohort of 45 patients was treated with CCH at the metacarpophalangeal(MCP)joint and the proximal interphalangeal(PIP)joint and underwent systematic reevaluation.The study adhered to multicenter trial protocols,and assessments were conducted at 12 wk,7 years,and 10 years post-surgery.RESULTS Thirty-seven patients completed the 10-year follow-up.At 10 years,patients treated at the PIP joint exhibited a 100%recurrence.However,patients treated at the MCP joint only showed a 50%recurrence.Patient satisfaction varied,with a lower satisfaction reported in PIP joint cases.Recurrence exceeding 20 degrees on the total passive extension deficit was observed,indicating a challenge for sustained efficacy.Significant differences were noted between outcomes at the 7-year and 10-year intervals.CONCLUSION CCH demonstrated sustained efficacy when applied to the MCP joint.However,caution is warranted for CCH treatment at the PIP joint due to a high level of recurrence and low patient satisfaction.Re-intervention is needed within a decade of treatment.

Characterization of N6-methyladenosine long non-coding RNAs in sporadic congenital cataract and age-related cataract

AIM:To characterize the N6-methyladenosine(m6A)modification patterns in long non-coding RNAs(lncRNAs)in sporadic congenital cataract(CC)and age-related cataract(ARC).METHODS:Anterior capsule of the lens were collected from patients with CC and ARC.Methylated RNA immunoprecipitation with next-generation sequencing and RNA sequencing were performed to identify m6A-tagged lncRNAs and lncRNAs expression.Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and Gene Ontology annotation were used to predict potential functions of the m6A-lncRNAs.RESULTS:Large amount of m6A peaks within lncRNA were identified for both CC and ARC,while the level was much higher in ARC(49870 peaks)than that in CC(18688 peaks),yet those difference between ARC in younger age group(ARC-1)and ARC in elder age group(ARC-2)was quite slight.A total of 1305 hypermethylated and 1178 hypomethylated lncRNAs,as well as 182 differential expressed lncRNAs were exhibited in ARC compared with CC.On the other hand,5893 hypermethylated and 5213 hypomethylated lncRNAs,as well as 155 significantly altered lncRNA were identified in ARC-2 compared with ARC-1.Altered lncRNAs in ARC were mainly associated with the organization and biogenesis of intracellular organelles,as well as nucleotide excision repair.CONCLUSION:Our results for the first time present an overview of the m6A methylomes of lncRNA in CC and ARC,providing a solid basis and uncovering a new insight to reveal the potential pathogenic mechanism of CC and ARC.

Epdemiology and Treatment of Pseudarthrosis of Long Bones in the Servce D Orthopedics-Traumatology of the University Hospital of Donka

Introduction: Pseudarthrosis (PSA) of the diaphysis of long bones still remains a current problem, despite improvements in the treatment of these fractures. Our study aims to study the epidemiological and therapeutic aspects of PSA of the diaphysis of long bones. Method: This retrospective work concerns 30 cases of non-union of the diaphysis of long bones treated in the orthopedic and trauma surgery department at Donka National Hospital, during a period of 18 months from January 1, 2019 to June 30, 2020. Results: We recruited 30 patients, 80% of whom were male, with an average age of 39.9 years. Public road accidents (AVP) represented the main cause of fractures of the diaphysis of long bones 87%, they were open in 25 cases or 83%. The fractures were located in the middle 1/3 of the diaphysis of the long bones in 50% of cases. Treatment of initial fractures was traditional in 21 cases, orthopedic in 2 cases and surgical in 7 cases. It was aseptic nonunion in 28 cases (93%) and septic nonunion in 2 cases. They were hypertrophic in 7 cases, slightly hypertrophic in 5 cases, oligotrophic in 11 cases, atrophic in 6 cases and with bone defect in 1 case. The treatment was based on osteosynthesis including 16 cases of screwed “PV” plate: 7 cases of centromedullary “ECM” nailing, 2 cases of external fixator, 1 case of broaching and 4 cases of Plastering. The results according to ASAMI criteria on an anatomical level were excellent in 19 cases, good in 3 cases and poor in 3 cases, with a union rate of 76%. And 5 patients undergoing consolidation. Conclusion: Based on the literature data and the experience of our department, the true treatment of PSA requires correct management of the initial fracture without forgetting the interest in preventing AVP which appears to be an element essential, making it possible to reduce the incidence of fractures of the diaphysis.

Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning methods.However,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive users.To better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal context.First,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window.Subsequently,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active users.We further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,respectively.Extensive experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation metrics.More specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

Construction of prognostic markers for gastric cancer and comprehensive analysis of pyroptosis-related long non-coding RNAs

BACKGROUND China's most frequent malignancy is gastric cancer(GC),which has a very poor survival rate,and the survival rate for patients with advanced GC is dismal.Pyroptosis has been connected to the genesis and development of cancer.The function of pyroptosis-related long non-coding RNAs(PRLs)in GC,on the other hand,remains uncertain.AIM To explore the construction and comprehensive analysis of the prognostic characteristics of long non-coding RNA(lncRNA)related to pyroptosis in GC patients.METHODS The TCGA database provided us with 352 stomach adenocarcinoma samples,and we obtained 28 pyroptotic genes from the Reactome database.We examined the correlation between lncRNAs and pyroptosis using the Pearson correlation coefficient.Prognosis-related PRLs were identified through univariate Cox analysis.A predictive signature was constructed using stepwise Cox regression analysis,and its reliability and independence were assessed.To facilitate clinical application,a nomogram was created based on this signature.we analyzed differences in immune cell infiltration,immune function,and checkpoints between the high-risk group(HRG)and low-risk group(LRG).RESULTS Five hundred and twenty-three PRLs were screened from all lncRNAs(absolute correlation coefficient>0.4,P<0.05).Nine PRLs were included in the risk prediction signature that was created through stepwise Cox regression analysis.We determined the risk score for GC patients and employed the median value as the dividing line between HRG and LRG.The ability of the risk signature to predict the overall survival(OS)of GC is demonstrated by the Kaplan-Meier analysis,risk curve,receiver operating characteristic curve,and decision curve analysis curve.The risk signature was shown to be an independent prognostic factor for OS in both univariate and multivariate Cox regression analyses.HRG showed a more efficient local immune response or modulation compared to LRG,as indicated by the predicted signal pathway analysis and examination of immune cell infiltration,function,and checkpoints(P<0.05).CONCLUSION In general,we have created a brand-new prognostic signature using PRLs,which may provide ideas for immunotherapy in patients with GC.

A three-dimensional co-continuous network structure polymer electrolyte with efficient ion transport channels enabling ultralong-life all solid-state lithium metal batteries

Solid polymer electrolytes(SPEs)have emerged as one of the most promising candidates for the construction of solid-state lithium batteries due to their excellent flexibility,scalability,and interface compatibility with electrodes.Herein,a novel all-solid polymer electrolyte(PPLCE)was fabricated by the copolymer network of liquid crystalline monomers and poly(ethylene glycol)dimethacrylate(PEGDMA)acts as a structural frame,combined with poly(ethylene glycol)diglycidyl ether short chain interspersed serving as mobile ion transport entities.The preparaed PPLCEs exhibit excellent mechanical property and out-standing electrochemical performances,which is attributed to their unique three-dimensional cocontinuous structure,characterized by a cross-linked semi-interpenetrating network and an ionic liquid phase,resulting in a distinctive nanostructure with short-range order and long-range disorder.Remarkably,the addition of PEGDMA is proved to be critical to the comprehensive performance of the PPLCEs,which effectively modulates the microscopic morphology of polymer networks and improves the mechanical properties as well as cycling stability of the solid electrolyte.When used in a lithiumion symmetrical battery configuration,the 6 wt%-PPLCE exhibites super stability,sustaining operation for over 2000 h at 30 C,with minimal and consistent overpotential of 50 mV.The resulting Li|PPLCE|LFP solid-state battery demonstrates high discharge specific capacities of 160.9 and 120.1 mA h g^(-1)at current densities of 0.2 and 1 C,respectively.Even after more than 300 cycles at a current density of 0.2 C,it retaines an impressive 73.5%capacity.Moreover,it displayes stable cycling for over 180 cycles at a high current density of 0.5C.The super cycle stability may promote the application for ultralong-life all solid-state lithium metal batteries.

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Integrating disulfidptosis-related long noncoding RNAs in colorectal cancer prognosis:A path to precision medicine

This commentary explores the burgeoning field of disulfidptosis-related long noncoding RNAs(lncRNAs)in the prognosis and therapeutic targeting of colorectal cancer(CRC).By evaluating recent research,including the pivotal study"Predicting colorectal cancer prognosis based on long noncoding RNAs of disulfidptosis genes"by Wang et al,this analysis underscores the critical role of lncRNAs in deciphering the molecular complexities of CRC.Highlighting the innovative methodologies and significant findings,I discuss the implications for patient survival,therapeutic response,and the potential of lncRNAs as biomarkers for precision medicine.The integration of bioinformatics,clinical databases,and molecular biology in these studies offers a promising avenue for advancing CRC treatment strategies and improving patient outcomes.