bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single-port Thoracoscopic Laser Sympathicotomy for Primary Hyperhidrosis: A Safe and Minimally Invasive Approach With Favorable Short-term Outcomes
Authors: Do Tat Thanh , DOAN Quoc Hung , Cong Huu Nguyen , Nguyen Thi Minh Chinh , DOAN Vu Tu Quyen , Vuong Nguyen Lam 
Published: November 2024

Objective:
Thoracoscopic sympathicotomy is a well-established treatment for severe palmar hyperhidrosis. This study evaluates the safety and efficacy of a novel one-stage, bilateral, single-port laser sympathicotomy with minimal dissection.

Methods:
We retrospectively reviewed 73 patients with severe palmar hyperhidrosis who underwent this novel surgical technique between June 2023 and October 2023. Outcomes included complications, recurrent hyperhidrosis, and compensatory hyperhidrosis (CH). Hyperhidrosis severity was assessed using the hyperhidrosis disease severity score (HDSS).

Results:
The mean patient age was 21.3 ± 7.7 years, with a female predominance (52.1%). Sympathicotomy was performed at the T3 level only in 46 patients (63%), with the remaining undergoing sympathicotomy at both T3 and T4 levels. Median cauterization and operating times were 78 seconds and 8.7 minutes, respectively. No complications occurred. Two patients experienced recurrent hyperhidrosis during a median follow-up of 9 months, both were mild (HDSS grade 1). CH developed in 41 patients (56.2%), with all occurring within the first month postoperatively. The most common sites for CH were the back (51.2%), chest (43.9%), thigh (41.5%), abdomen (36.6%), and lower leg (36.6%). The majority of patients with CH (95.1%) reported mild (HDSS grade 1) or moderate (HDSS grade 2) hyperhidrosis. A higher body mass index was a significant risk factor for CH (odds ratio: 1.36, 95% CI: 1.12-1.71 for every 1 kg/m2 increase).

Conclusions:
This minimally invasive, single-port thoracoscopic laser sympathicotomy appears to be safe and effective for patients with palmar primary hyperhidrosis. Future studies should investigate long-term outcomes and refine the technique to minimize invasiveness even further.
Differentiation of Horner Syndrome and Physiological Anisocoria by Automated Pupillometry
Authors: Disse Leah R. , Bockisch Christopher J. , Weber Konrad P. , Fierz Fabienne C. 
Published: November 2024

Background:
The differentiation of Horner syndrome from physiological anisocoria is important yet clinically challenging. We investigated the diagnostic accuracy of pupillometry to discriminate Horner syndrome from physiological anisocoria compared to pharmacological testing with the alpha-2-agonist apraclonidine, which is considered the current gold standard.

Methods:
Forty-four adult patients, mostly referred to our neuro-ophthalmology service for evaluation of anisocoria, were included. Automated binocular pupillometry was performed under standardized light conditions before and >30 minutes after instillation of 1% apraclonidine eye drops. A positive apraclonidine test indicating unilateral Horner syndrome was defined as an increase of pupil size in the smaller pupil and decrease of size in the larger pupil. Receiver operator characteristic curves were calculated to find the best pupillometric parameter discriminating Horner syndrome from physiological anisocoria.

Results:
We found that the parameters measuring the pupillary dilation lag using pupillometry could reliably discriminate Horner syndrome from physiological anisocoria compared to pharmacological testing. Calculating the change of anisocoria at 3–4 seconds after light-off relative to the anisocoria at the end of the light-on period (Δ3-4) may be most suitable to rule out Horner syndrome reaching a sensitivity of 95% and specificity of 68% using a cutoff of 0.35 mm.

Conclusions:
Our results indicate that pupillometry is a robust tool to measure the dilation lag in Horner syndrome and, therefore, to distinguish pathological from physiological anisocoria obviating pharmacological testing. The high sensitivity of the test will allow to identify the patients with Horner syndrome requiring further investigation.
Efficacy and safety of stem cell therapy for diabetic kidney disease: a systematic review and meta-analysis
Authors: Du Hongyu , Xie Chen , Yuan Yiqin , Luo Yun , Cao Jinguo , Li Zhihai , Yuan Jiayi , Li Wei 
Published: November 2024

AbstractBackground/ObjectivesAnimal studies have demonstrated the ability of stem cell therapy (SCT) to treat diabetic kidney disease (DKD). However, the safety and efficacy of SCT in patients with DKD remain unclear. This systematic review and meta-analysis aimed to investigate the safety and efficacy of SCT in patients with DKD.MethodsA comprehensive and systematic literature search was conducted using PubMed, EMBASE, Cochrane Library, and Web of Science to identify articles on SCT for DKD published up to March 2024. RevMan V.5.4 software was used for statistical analysis.ResultsWe identified four studies that included 90 participants, 53 (58%) of whom underwent SCT. SCT improved estimated glomerular filtration rate (eGFR) (mean difference [MD] = 0.41, 95% confidence interval [CI]: 0.08–0.74; p < 0.05), serum creatinine (SCr) reduction (standardized MD = -0.65, 95%CI: -1.19 to -0.1, p < 0.05), and microalbuminuria (MAU) (MD = -32.10, 95% CI: -55.26–8.94; p < 0.05) compared to the control group, but did not improve urine microalbumin/creatinine ratio (UACR) (MD = -63.36, 95% CI: 194.52–67.79, p = 0.56) or blood sugar (MD = 0.49, 95% CI: 4.16–2.01, p = 0.49). In addition, the incidence of adverse events was significantly high in both groups (risk ratio = 0.93; 95% CI: 0.74–1.17; p = 0.54); there was no significant difference regarding I2= 0%).ConclusionsSCT can safely and effectively improve eGFR and SCr levels by lowering the MAU but cannot improve UACR and blood sugar levels.
The Association of Long-Term Body Mass Index Variability with the Development of HFpEF and HFrEF Across Patterns of Weight Change
Authors: Yu Zeshui , Chen Yuqing , Miranda Oshin , Qi Meiyuzhen , Zhang Manling , Feng Ning , Ryan Timothy P , Schloot Nanette Cathrin , Chen Yu , Sam Flora , Wang Lirong 
Published: November 2024

AbstractBackgroundRecent studies have shown BMI variability is risk factor for various adverse cardiovascular outcomes. However, the specific associations between BMI variability and the risk of developing HFpEF versus HFrEF, particularly across multiple weight change trends, remain unexplored.Methods and ResultsWe identified a cohort of 52,286 eligible patients with overweight or obesity grouped into three categories based on their BMI change patterns over five years: weight loss, stable weight, and weight gain. BMI variability was assessed in the same 5-year period using average successive variability (ASV). These patients were subsequently followed to monitor the incidence of HFpEF and HFrEF. Cox regression models were applied to examine the differential association between BMI variability and HFpEF and HFrEF risk. Over a median follow-up of 4.81 years, 2,295 patients developed HFpEF, and 1,189 developed HFrEF. After adjusting for relevant confounders, elevated BMI variability was associated with an increased risk of HFpEF. The hazard ratios (HRs) of HFpEF for each 1-SD increment in ASV of BMI were 1.27 (95% CI, 1.10-1.47) in the weight loss group and 1.22 (95% CI, 1.09-1.37) in the stable weight group. Additionally, when analyzed as a binary variable divided by the median, BMI variability above the median was associated with higher risks of HFpEF compared to those below the median, with the corresponding HRs being 1.46 (95% CI, 1.20-1.77) for the weight loss group and 1.17 (95% CI, 1.04-1.31) for the stable weight group.ConclusionsIn this large cohort of patients living with overweight or obesity, greater BMI variability was significantly associated with a higher risk of developing HFpEF compared to patients with reduced and stable weight over time.Clinical Perspective What’s new?1. In patients with weight loss and stable weight, those with higher BMI variability have an increased risk of developing incident HFrEF compared to those experiencing lower BMI variability, after adjusting all potential confounding variables.2. In patients with weight gain, BMI variability was not significantly linked to the risk of developing HFpEF or HFrEF. However, a larger increase in delta BMI was significantly associated with a higher risk of incident HFpEF and HFrEF in this group.What are the clinical implications?1. Promoting the importance of stable and consistent weight management strategies to reduce heart failure risk, particularly by minimizing BMI variability in patients undergoing weight loss or maintaining stable weight.
Late Quaternary millennial-scale stability of the feeding strategy of the gray brocket deer [Subulo gouazoubira(G. Fisher, 1814); Mammalia] in Southeastern Brazil
Authors: Hubbe Alex , Pestle William J. , Hubbe Mark , Amirali Avaaz , Chim Eliane Nunes , Oliveira Rodrigo Elias , Pinto Sandoval Eluzai Dinai , Duarte José Maurício Barbanti , Neves Walter A 
Published: November 2024

AbstractThere is limited evidence, both contemporary and archaeological/paleontological, regarding the feeding ecology of the gray brocket deer (Subulo gouazoubira) in Brazil’s Cerrado biome. Using stable carbon (δ13C) and oxygen (δ18O) isotope analyses of dental enamel bioapatite from individuals recovered from Cuvieri Cave, Minas Gerais, we explore dietary and environmental changes between the Late Pleistocene and Holocene epochs in this biome. The average Pleistocene δ13C was -10.9±1.7‰ and δ18Odwwas -7.9±0.4‰, while Holocene individuals averaged a δ13C of -10.5±1.2‰ and δ18Odwof -5.8±0.9‰. The δ13C data suggest no significant shift in feeding strategy between the Late Pleistocene and Holocene, with both sets of individuals displaying δ13C values consistent with browsing in environments other than closed canopy forests. However, δ18Odwvalues indicate an approximate temperature increase of 3.5°C between the two epochs and/or a reduction in precipitation. This study highlights the stability in the feeding behavior of the gray brocket deer despite biotic and abiotic changes between the epochs.
Integrative proteo-genomic profiling uncovers key biomarkers of lapatinib resistance in HER2-positive breast cancer
Authors: Steggall J , Rajeeve V , Al-Subaie N , Hayat A 
Published: November 2024

AbstractDrug resistance is a major obstacle to the long-term effectiveness of cancer therapies. Approximately 70% of breast cancer patients relapse after 5 years of treatment, and the lack of biomarkers associated with drug resistance translates to poor prognosis in clinic. Previous research has utilised omics approaches to uncover biomarkers driving drug resistance, with a strong emphasis on genetic mutations. Here, we identified a nine-marker signature associated with resistance to lapatinib in a HER2-positive breast cancer model using a target discovery approach by employing an integrative multi-omics strategy, combining ATAC-seq, RNA-seq, and proteomics. We found that 7 markers in the drug resistance-signature had not been previously found to be implicated in HER2 positive breast cancer. We counterintuitively found that drug resistant cells have restrictive chromatin accessibility with reduced gene expression associated with limited total proteome changes. However, upon closer look, we identified that the drug resistance-signature had increased chromatin accessibility closer to the transcriptional start sites of those genes and are highly differentially expressed across the three datasets. Our data show that despite the overall transcriptional and proteomic landscape showing limited changes, there are several markers that are highly expressed, which correlate with increased anchorage-independent and invasive phenotypein vitroin lapatinib resistant cells compared to cancer cells. Our results demonstrate that disease aggressiveness can be related to reduced chromatin and gene expression dynamics. We anticipate that the resistant signature identified here using integrative target discovery approach can be applied to complex, representative models and validated before they can be targeted by suitable therapeutic agents.
Healthy B cells: allies or adversaries of CAR-T cell immunotherapy?
Authors: Paixão Emanuelle A. , Barros Luciana R. C. , Fassoni Artur C. , Almeida Regina C. 
Published: November 2024

Chimeric antigen receptor (CAR)-T cell immunotherapy has achieved significant success against various haematological cancers, including B-cell malignancies. Its efficacy against B-cell cancers is influenced by the presence of healthy B-cells expressing the target antigen, and B-cell aplasia (BCA) serves as an indicator of successful therapy outcome. However, the precise influence of healthy B-cells on thein vivodynamics of CAR-T cells and their ultimate impact on therapy outcomes remain unclear. Here, we propose a mathematical model to describe CAR-T cell immunotherapy in B-cell cancer patients. Our model successfully captured the interactions between different CAR-T cell phenotypes, tumour cells, and healthy B cells in patients who achieved a complete response. Using these cases, we constructed virtual scenarios to investigate how variations in baseline tumour and healthy B-cell populations, along with patient-specific factors related to CAR-T cell expansion and B-cell influx from the bone marrow, affect treatment outcomes. Our results suggest that the onset and duration of BCA is a patient-specific feature that depends primarily on the continuous influx of newly generated B cells, their proliferative capacity, and the expansion and cytotoxicity of CAR-T cells.Statement of significanceThis study presents a significant advancement in understanding the dynamics of CAR-T cell immunotherapy in B-cell malignancies by introducing a mathematical model that captures the complex interactions between CAR-T cells, tumour cells, and healthy B cells. The model provides crucial insights into how patient-specific factors, such as baseline tumour burden, B-cell populations, and CAR-T cell expansion, influence treatment outcomes, including the onset and duration of B-cell aplasia (BCA), a key marker of therapeutic success. Healthy B cells can act as allies, adversaries, or have a neutral effect on the therapy, depending on the tumour burden. These findings highlight the importance of personalised approaches in CAR-T cell immunotherapy, offering potential pathways to optimise treatment strategies for improved efficacy and patient outcomes.
MAGE: Monte Carlo method for Aberrant Gene Expression
Authors: Beltran Matthew , Joh Richard I. 
Published: November 2024

AbstractIdentifying genes that are aberrantly expressed is an important first step in the diagnosis and treatment of many diseases. Conventionally, differential expression (DE) analysis is used to screen gene expression profiles to identify functionally associated genes. DE often relies on the variance and fold change in expression from individual genes, which does not consider the expression of all other genes within the profile. When the overall gene expression is skewed, DE does not capture outliers in gene expression. To address this, we have developed a non-parametric DE method based on the probability density for an entire expression profile to select genes that deviate from the global distribution between two gene expression profiles with multiple replicates. Rather than assuming a particular distribution of expression per gene, our method assumes that aberrantly expressed genes (AEGs) will exhibit expression patterns distinguishable from non-AGEs which make up the majority of the profile.Here we introduce our nonparametric method (MAGE: Monte Carlo method for aberrant gene expression) and demonstrate that MAGE can identify AEGs that are not found by conventional DE analyses. The main feature of MAGE is (1) identifying outliers based on the expression profile of all genes rather than performing DE analyses on a per-gene basis and (2) consideration of the variance in expression between two different conditions. We also compared our results with traditional DE analysis as well as density-based clustering methods. MAGE produces consistent results in a variety of conditions and performs conservatively with the addition of noise. We also applied MAGE to single-cell RNA-seq samples and demonstrated that the analysis is robust with subsampling.
ScisTree2: An Improved Method for Large-scale Inference of Cell Lineage Trees and Genotype Calling from Noisy Single Cell Data
Authors: Zhang Haotian , Zhang Yiming , Gao Teng , Wu Yufeng 
Published: November 2024

AbstractIn a multicellular organism, cell lineages share a common evolutionary history, which has important applications in the study of development, aging, and cancer. Cell lineage tree is a model for the evolutionary history of cells sampled from an organism. The inference of cell lineage trees has been made possible by recent developments in single-cell sequencing. However, single-cell data is well known to be sparse and noisy. Moreover, the size of single cell data is increasing rapidly, both in the number of assayed cells and the amount of genomic information captured per cell. Thus, accurate inference of cell lineage tree from large single-cell data is a computational challenge. In this paper, we present ScisTree2, a fast and accurate cell lineage tree inference and genotype calling approach based on the infinite sites model. Similar to existing maximum likelihood-based inference approaches such as ScisTree, ScisTree2 relies on local search for finding optimal trees. Compared to the original ScisTree, the main new feature of ScisTree2 is a local search based on subtree prune and regraft (SPR) that is not implemented in the original ScisTree due to its high computational cost. This new local search is more general than that in ScisTree and significantly improves the accuracy of cell lineage tree inference. The key technical contributions of this work are the efficient SPR local search algorithms that can scale to very large data. ScisTree2 can call single cell genotypes based on the inferred cell lineage tree. Experiments on simulated and real biological data show that ScisTree2 achieves better overall accuracy while significantly outperforms in efficiency when compared with existing methods. To the best of our knowledge, ScisTree2 is the first model-based cell lineage tree inference and genotype calling approach that can infer trees with very large data with, e.g., tens of thousands of cells.
AI Assisted Native Proteomics: Delineating Ribosomal Protein Conformations Pre- and Post-Assembly
Authors: Zhang Wenjing , Sun Chen , Xu Zhang , Xu Wei 
Published: November 2024

AbstractThe simultaneous identification of proteins as well as their conformations in a biological system would greatly enhance our understanding of cellular mechanisms and disease. As an emerging technique, native proteomics analyzes proteins in their native states, facilitating the acquisition of protein stoichiometry, post-translational modifications (PTMs), and interactions with ligands. However, revealing protein conformations at the proteome scale remains a significant challenge. In this study, we propose an AI-assisted native proteomics method that integrates a protein structure prediction (PSP) module with top-down proteomics (TDP) and native mass spectrometry (nMS) to acquire both proteome identity and conformations. First, protein sequences and PTMs are obtained using the TDP method, while protein solvent-accessible surface area (SASA) is measured in parallel by nMS. These data are then input into the PSP module to acquire the most probable conformation of the protein under nMS experimental conditions. We validate the feasibility and accuracy of this method through the analysis of both a globular protein and an intrinsically disordered protein. Additionally, we apply this approach to delineate the conformations of ribosomal proteins pre- and post-assembly. Interactions of ribosomal proteins with drug molecules are also explored. By enabling proteome identification and conformation characterization from relatively small amounts of endogenous proteins, this method would bridge the gap between structural biology and conventional proteomics technologies.
Multi-source photographic evidence to assess corridor use, crop-raiding behaviour, and body injuries in Asian elephants
Authors: Anoop NR , Muneer PK , Madhavan M , Hatwar Anikethan , Ganesh T 
Published: November 2024

AbstractThis study investigated the use of multi-sourced photographs, such as those from social media, camera traps, and field surveys, for individual identification of male Asian elephants to study their corridor use, crop-raiding behaviour, occurrence of musth, ranging and external body injuries in the Thirunelli-Kudrakote elephant corridor in India’s Western Ghats. A total of 330 images and videos were analysed over 11 years (January 2014 to March 2024), leading to the identification of 27 male elephants from the corridor. Eleven individuals were observed across multiple years, demonstrating their fidelity to the area. Ten elephants were observed engaging in crop-raiding, with four consistently raiding crops within the corridor. The study also assessed the home range of 6 individuals in the corridor and adjacent landscapes based on individual identification. Overall, the study shows that identifying individual elephants across different locations and times provides valuable data and is a practical, cost-effective method for investigating a wide range of questions concerning the ecology and behaviour of Asian elephants. The findings underscore the potential of citizen science initiatives to enhance elephant research across elephant-range countries.
Dual Enzyme Inhibition Strategy for Type 2 Diabetes: Targeting LDH-A and α-Amylase with Flavonoid-Acarbose Combinations
Authors: Bose Adrija , Gupta Suvroma 
Published: November 2024

AbstractDiabetes mellitus is a complex and widespread disease affecting over 100 million people globally. It increases the risk of severe complications such as heart attack, neuropathy, and retinopathy. While various therapies aim to manage the disease, one effective approach involves reducing the activity of starch-degrading enzymes, such as α-amylase, to limit the amount of free glucose in the body. Another target is lactate dehydrogenase-A (LDH-A), an enzyme involved in gluconeogenesis through its role in generating pyruvate.In recent years, flavonoids have gained significant attention for their potential to inhibit both α-amylase and LDH-A. This study investigates the flavonoids present in green and black tea for their ability to inhibit porcine pancreatic amylase (PPA) and LDH-A, comparing their efficacy to established anti-diabetic drugs such as Acarbose and Metformin using computational methods.In vitro experiments demonstrated that Quercetin shows potent anti-LDHA-A activity with an IC50of 4.161 µM. Quercetin inhibits PPA with an IC50value of 20 µM, while eriodyctiol and myricetin exhibit IC50values of 22 µM and 24 µM, respectively. Additionally, quercetin was found to synergistically enhance the inhibitory effects of the commonly used α-amylase inhibitor, Acarbose.