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Repair techniques for short-distance peripheral nerve defects, including adjacent joint flexion to reduce the distance between the nerve stump defects, ＂nerve splint＂ suturing, and nerve sle eve connection, have some disadvantages. Therefore, we designed a repair technique involving intraoperative tension-free application of a nerve elongator and obtained good outcomes in the repair of short-distance peripheral nerve defects in a previous animal study. The present study compared the clinical outcomes between the use of this nerve elongator and performance of the conventional method in the repair of short-distance transection injuries in human elbows. The 3-, 6-, and 12-month postoperative follow-up results demonstrated that early neurological function recovery was better in the nerve elongation group than in the conventional group, but no signif- icant difference in long-term neurological function recovery was detected between the two gro ups. In the nerve elongation group, the nerves were sutured without tension, and the duration of postoperative immobilization of the elbow was decreased. Elbow function rehabilitation was significantly better in the nerve elongation group than in the control group. Moreover, there were no security risks. The results of this study confirm that the use of this nerve elongator for repair of short-distance peripheral nerve defects is safe and effective.

Large language models (LLMs) often generate fluent but factually incorrect outputs, known as hallucinations, which undermine their reliability in real-world applications. While uncertainty estimation has emerged as a promising strategy for detecting such errors, current metrics offer limited interpretability and lack clarity about the types of uncertainty they capture. In this paper, we present a systematic framework for decomposing LLM uncertainty into four distinct sources, inspired by previous research. We develop a source-specific estimation pipeline to quantify these uncertainty types and evaluate how existing metrics relate to each source across tasks and models. Our results show that metrics, task, and model exhibit systematic variation in uncertainty characteristic. Building on this, we propose a method for task specific metric/model selection guided by the alignment or divergence between their uncertainty characteristics and that of a given task. Our experiments across datasets and models demonstrate that our uncertainty-aware selection strategy consistently outperforms baseline strategies, helping us select appropriate models or uncertainty metrics, and contributing to more reliable and efficient deployment in uncertainty estimation.

Prompt optimization algorithms for Large Language Models (LLMs) excel in multi-step reasoning but still lack effective uncertainty estimation. This paper introduces a benchmark dataset to evaluate uncertainty metrics, focusing on Answer, Correctness, Aleatoric, and Epistemic Uncertainty. Through analysis of models like GPT-3.5-Turbo and Meta-Llama-3.1-8B-Instruct, we show that current metrics align more with Answer Uncertainty, which reflects output confidence and diversity, rather than Correctness Uncertainty, highlighting the need for improved metrics that are optimization-objective-aware to better guide prompt optimization. Our code and dataset are available at https://github.com/0Frett/PO-Uncertainty-Benchmarking.

Prompt optimization algorithms for Large Language Models (LLMs) excel in multi-step reasoning but still lack effective uncertainty estimation. This paper introduces a benchmark dataset to evaluate uncertainty metrics, focusing on Answer, Correctness, Aleatoric, and Epistemic Uncertainty. Through analysis of models like GPT-3.5-Turbo and Meta-Llama-3.1-8B-Instruct, we show that current metrics align more with Answer Uncertainty, which reflects output confidence and diversity, rather than Correctness Uncertainty, highlighting the need for improved metrics that are optimization-objective-aware to better guide prompt optimization. Our code and dataset are available at https://github.com/0Frett/PO-Uncertainty-Benchmarking.

This research paper addresses the challenge of modality mismatch in multimodal learning, where the modalities available during inference differ from those available at training. We propose the Text-centric Alignment for Multi-Modality Learning (TAMML) approach, an innovative method that utilizes Large Language Models (LLMs) with in-context learning and foundation models to enhance the generalizability of multimodal systems under these conditions. By leveraging the unique properties of text as a unified semantic space, TAMML demonstrates significant improvements in handling unseen, diverse, and unpredictable modality combinations. TAMML not only adapts to varying modalities but also maintains robust performance, showcasing the potential of foundation models in overcoming the limitations of traditional fixed-modality frameworks in embedding representations. This study contributes to the field by offering a flexible, effective solution for real-world applications where modality availability is dynamic and uncertain.

Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training. We present LiveCLKTBench, an automated generation pipeline specifically designed to isolate and measure cross-lingual knowledge transfer. Our pipeline identifies self-contained, time-sensitive knowledge entities from real-world domains, filters them based on temporal occurrence, and verifies them against the model's knowledge. The documents of these valid entities are then used to generate factual questions, which are translated into multiple languages to evaluate transferability across linguistic boundaries. Using LiveCLKTBench, we evaluate several LLMs across five languages and observe that cross-lingual transfer is strongly influenced by linguistic distance and often asymmetric across language directions. While larger models improve transfer, the gains diminish with scale and vary across domains. These findings provide new insights into multilingual transfer and demonstrate the value of LiveCLKTBench as a reliable benchmark for future research.

In this work, we conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes. Each of these tasks corresponds to unique optimization domains, and LLMs are required to execute these tasks with interactive prompting. That is, in each optimization step, the LLM generates new solutions from the past generated solutions with their values, and then the new solutions are evaluated and considered in the next optimization step. Additionally, we introduce three distinct metrics for a comprehensive assessment of task performance from various perspectives. These metrics offer the advantage of being applicable for evaluating LLM performance across a broad spectrum of optimization tasks and are less sensitive to variations in test samples. By applying these metrics, we observe that LLMs exhibit strong optimization capabilities when dealing with small-sized samples. However, their performance is significantly influenced by factors like data size and values, underscoring the importance of further research in the domain of optimization tasks for LLMs.

Aim： To develop a combined population pharmacokinetic model （PPK） to assess the magnitude and variability of exposure to both clozapine and its primary metabolite norclozapine in Chinese patients with refractory schizophrenia via sparse sampling with a focus on the effects of covariates on the pharmacokinetic parameters. Methods： Relevant patient concentration data （eg, demographic data, medication history, dosage regimen, time of last dose, sampling time, concentrations of clozapine and norclozapine, etc） were collected using a standardized data collection form. The demographic characteristics of the patients, including sex, age, weight, body surface area, smoking status, and information on concomitant medi- cations as well as biochemical and hematological test results were recorded. Persons who had smoked 5 or more cigarettes per day within the last week were defined as smokers. The concentrations of clozapine and norclozapine were measured using a HPLC system equipped with a UV detector. PPK analysis was performed using NONMEM. Age, weight, sex, and smoking status were evaluated as main covariates. The model was internally validated using normalized prediction distribution errors. Results： A total of 809 clozapine concentration data sets and 808 norclozapine concentration data sets from 162 inpatients （74 males, 88 females） at multiple mental health sites in China were included. The one-compartment pharmacokinetic model with mixture error could best describe the concentration-time profiles of clozapine and norclozapine. The population-predicted clearance of clozap- ine and norclozapine in female nonsmokers were 21.9 and 32.7 L/h, respectively. The population＊predicted volumes of distribution for clozapine and norclozapine were 526 and 624 L, respectively. Smoking was significantly associated with increases in the clearance （clozapine by 45%; norclozapine by 54.3%）. The clearance was significantly greater in males than in females （clozapine by 20.8%; nor- clozapine by 24.2%）. The clearance of clozapine and norclozapine did not differ significantly between Chinese patients and American patients. Conclusion： Smoking and male were significantly associated with a lower exposure to clozapine and norclozapine due to higher clearance. This model can be used in individualized drug dosing and therapeutic drug monitoring.

Aim： Human CXCR3, a seven-transmembrane segment （7TMS）, is predominantly expressed in Th1-mediated responses. Interferon-y-inducible protein 10 （IP-10） is an important ligand for CXCR3. Their interaction is pivotal for leukocyte migration and activation. Tyrosine sulfation in 7TMS is a posttranslational modification that contributes substantially to ligand binding. We aimed to study the role of tyrosine sulfation of CXCR3 in the protein＇s binding to IP-10. Methods： Plasmids encoding CXCR3 and its mutants were prepared by PCR and site-directed mutagenesis. HEK 293T cells were transfected with plasmids encoding CXCR3 or its variants using calcium phosphate. Transfected cells were labeled with [^35S]-cysteine and methionine or [35S]-Na2SO3 and then analyzed by immunoprecipitation to measure sulfation. Experiments with ^125 I-labeled IP-10 were carried out to evaluate the affinity of CXCR3 for its ligand. Calcium influx assays were used to measure intercellular signal transduction. Results： Our data show that sulfate moieties are added to tyrosines 27 and 29 of CXCR3. Mutation of these two tyrosines to phenylalanines substantially decreases binding of CXCR3 to IP-10 and appears to eliminate the associated signal transduction. Tyrosine sulfation of CXCR3 is enhanced by tyrosyl protein sulfotransferases （TPSTs）, and it is weakened by shRNA constructs. The binding ability of CXCR3 to IP-10 is increased by TPSTs and decreased by shRNAs. Conclusion： This study identifies two sulfated tyrosines in the N-terminus of CXCR3 as part of the binding site for IP-10, and it underscores the fact that tyrosine sulfation in the N-termini of 7TMS receptors is functionally important for ligand interactions. Our study suggests a molecular target for inhibiting this ligand-receptor interaction.

Available research about the anatomic patterns of intertrochanteric fractures is lacking, and fracture mapping has not previously been performed on intertrochanteric fractures. This study aimed to determine the major trajectories of intertrochanteric fracture lines using computed tomography data from a series of surgically treated patients. In this study, 504 patients with intertrochanteric fractures were retrospectively analyzed. Fracture patterns were graded according to Arbeitsgemeinschaft für Osteosynthesefragen (AO) classification. Fracture lines were transcribed onto proximal femoral templates and graphically superimposed to create a compilation of fracture maps that were subsequently divided into anterior, posterior, lateral, and medial fracture maps to create a three-dimensional (3D) pattern by reducing fragments in the 3D models. The fracture maps were then converted into frequency spectra. The major fracture patterns were assessed by focusing on the lateral femoral wall, lesser trochanter, intertrochanteric crest, and inner cortical buttress. Anterior, posterior, lateral, and medial fracture maps were created. The majority of fracture lines (85.9%, 433/504) on the anterior maps were along the intertrochanteric line where the iliofemoral ligament was attached. In the medial plane, the majority of fracture lines (49.0%, 247/504) shown on the frequency spectrum included the turning point involving the third quadrant. In the posterior plane, the majority of fracture lines (52.0%, 262/504) involved the intertrochanteric crest from the greater to the lesser trochanter. In the lateral plane, the majority of fracture lines (62.7%, 316/504) involved the greater trochanter at the gluteus medius attachment. The fracture patterns observed in the present study might be used to describe morphologic characteristics and aid with management strategies. Further classifications or modifications that incorporate the fracture patterns identified in this study may be used in future research.