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Artificial intelligence (AI) helps guide medical decisions that benefit individuals and populations and provides insights for optimizing various systems such as public health [3]. Since the release of the chat robot ChatGPT, this artificial intelligence technology has clearly had a significant impact on the way humans work [4]. [...]ChatGPT simulating SP was highly intelligent. [...]there were also some drawbacks, such as ChatGPT simulation of SP's responses being mechanical and rigid occasionally. [...]our results showed that ChatGPT simulating SP could assist in clinical training and education, thereby more effectively guiding doctors' clinical skills, optimizing the education system, and improving medical skills.

Background Tuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods often involving invasive surgery and being time-consuming. While various machine learning and statistical models have been proposed for TPE diagnosis, these methods are typically limited by complexities in data processing and difficulties in feature integration. Therefore, this study aims to develop a diagnostic model for TPE using ChatGPT-4, a large language model (LLM), and compare its performance with traditional logistic regression and machine learning models. By highlighting the advantages of LLMs in handling complex clinical data, identifying interrelationships between features, and improving diagnostic accuracy, this study seeks to provide a more efficient and precise solution for the early diagnosis of TPE. Methods We conducted a cross-sectional study, collecting clinical data from 109 TPE and 54 non-TPE patients for analysis, selecting 73 features from over 600 initial variables. The performance of the LLM was compared with logistic regression and machine learning models (k-Nearest Neighbors, Random Forest, Support Vector Machines) using metrics like area under the curve (AUC), F1 score, sensitivity, and specificity. Results The LLM showed comparable performance to machine learning models, outperforming logistic regression in sensitivity, specificity, and overall diagnostic accuracy. Key features such as adenosine deaminase (ADA) levels and monocyte percentage were effectively integrated into the model. We also developed a Python package ( https://pypi.org/project/tpeai/ ) for rapid TPE diagnosis based on clinical data. Conclusions The LLM-based model offers a non-surgical, accurate, and cost-effective method for early TPE diagnosis. The Python package provides a user-friendly tool for clinicians, with potential for broader use. Further validation in larger datasets is needed to optimize the model for clinical application.

Primary testicular lymphoma (PTL) is a rare lymphoma predominantly occurring in the elderly male population. It is characterized by a limited response to treatment and a heightened tendency towards relapse. Histologically, approximately 90% of PTL cases are classified as diffuse large B-cell lymphomas (DLBCL). Genetic features of PTL were delineated in a limited scope within several independent studies. Some of the articles which analyzed the genetic characterization of DLBCL have incorporated PTL samples, but these have been constrained by small sample sizes. In addition, there have been an absence of independent molecular typing studies of PTL. This report summarizes the common mutational features, copy number variations (CNVs) and molecular typing of PTL patients, based on whole-exome sequencing (WES) conducted on a cohort of 25 PTL patients. Among them, HLA, CDKN2A and MYD88 had a high mutation frequency. In addition, we found two core mutational characteristics in PTL including mutation in genes linked to genomic instability (TP53 and CDKN2A) and mutation in immune-related genes (HLA, MYD88, CD79B). We performed molecular typing of 25 PTL patients into C1 subtype with predominantly TP53 mutations and C2 subtype with predominantly HLA mutations. Notably, mutations in the TP53 gene predicted a poor outcome in most types of lymphomas. However, the C1 subtype, dominated by TP53 mutations, had a better prognosis compared to the C2 subtype in PTL. C2 subtype exhibited a worse prognosis, aligning with our finding that the mechanism of immune escape in PTL was primarily the deletions of HLA rather than PD-L1/PD-L2 alterations, a contrast to other DLBCLs. Moreover, we calculated the tumor mutation burden (TMB) and identified that TMB can predict prognosis and recurrence rate in PTL. Our study underscores the significance of molecular typing in PTL based on mutational characteristics, which plays a crucial role in prognostication and guiding therapeutic strategies for patients.

Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive form of non-Hodgkin lymphoma. B symptoms, including fever, night sweats and weight loss, are often overlooked but carry prognostic significance. The primary site of DLBCL also plays a crucial role in diagnosis and treatment decisions, and our study aims to explore the relationship among primary sites of DLBCL, the presence of B symptoms and patients’ survival. 15,267 patients’ data were analyzed in our study to investigate the potential relevance among B symptoms, primary sites and patients’ survival. We assessed the influence of primary site and B symptoms on survival with the use of Survival package and Kaplan–Meier survival analysis. Age, race and Ann-Arbor stage were significantly associated with the presence of B symptoms. The incidence rate of B symptoms varied across primary sites, bone marrow had the highest probability (43.75%), while the genital system had the lowest rate (13.58%). B symptoms were siginificant prognostic indicators among nodal DLBCL patients especially those involved lymph nodes of multiple regions, as well as patients with lymphoma originating from bone marrow, glands and respiration. Survival analysis revealed that bone marrow, genital organs, lymph nodes of multiple regions and head and neck are linked to poorer survival outcomes, which happen to be either the highest or lowest B symptom incidence sites. These findings highlight the prognostic importance of both primary tumor site and B symptom status in DLBCL patients.

Background For HER2-positive breast cancer (BC), patients who achieve pathologic complete response (pCR) are predicted to have better clinical outcomes. With the advent of the era of neoadjuvant chemotherapy (NAC) combined with targeted therapy, the pCR rate of HER2-positive BC has increased significantly, but about 50% of these patients still do not respond to targeted therapy. The dynamic changes in the tumor microenvironment (TME) may crucially influence pCR outcomes in HER2-positive BC. However, there is no specific immunophenotyping study that correlates with the efficacy during treatment. Methods We conducted a comprehensive spatial expression profiling analysis on 94 ROIs procured from 28 HER2-positive BC patients, encompassing both their initial diagnostic stage and postoperative status. Leveraging DSP techniques, we precisely mapped and quantified the TME at these critical time points for patients. Results Notably, we classified specimens into four groups: IM1, IM2, TM1, and TM2. IM1 and TM1, characterized by CD4⁺ naïve T cells and naïve B cells, showed higher pCR rates, whereas IM2 and TM2 exhibited lower rates. In addition, baseline IM1 patients who were classified as TM1 postoperatively achieved a 100% pCR rate, while baseline IM2 patients who were classified as TM2 postoperatively had a poor pCR rate of only 10%. The immunophenotypes defined by spatial transcriptomics were reproducible in both TCGA and single-cell datasets, and spatial analysis further revealed structured cellular transitions and functional heterogeneity at the tumor–immune interface. Conclusion Our dynamic immunophenotyping can provide prediction of the efficacy for NAC with trastuzumab in HER2-positive BC patients, and provide a basis for the selection of subsequent treatment regimens.

Ni/Pd co-modified graphene hydrogen storage materials were successfully prepared by a solvothermal method using NiCl2·6H2O and Pd(OAc)2 and reduced graphene oxide (rGO). By adjusting the hydrothermal temperature, Pd–Ni is successfully alloyed, and the size of the obtained nanoparticles is uniform. The electronic structure of Pd was changed by alloying, and the center of the D-band moved down, which promoted the adsorption of hydrogen. The NiPd-rGO-180 sample, in which 180 represents the solvothermal temperature in centigrade (°C), has the highest hydrogen storage capacity of 2.65 wt% at a moderate condition (RT/4MPa). The excellent hydrogen storage performance benefits from the synergistic hydrogen spillover effect of Pd–Ni bimetal. The calculated hydrogen adsorption energies of Ni2Pd2-rGO are within the ideal range (−0.20 to −0.60 eV) of hydrogen ads/desorption; however, the introduction of substrate defects and the cluster orientation alter the hydrogen adsorption energy. This work provides an effective reference for the design and optimization of carbon-based hydrogen storage materials.

The large volume expansion and self-locking phenomenon of metal hydride particles during hydrogen sorption often leads to a high stress concentration on the walls of a container, which may cause the collapse of the container. In present study, silicone oil was investigated as a glidant for a V-based BCC metal hydride bed to alleviate the stress concentration during hydrogen sorption. The results indicated that the addition of 5 wt% silicone oil slightly reduced the initial hydrogen storage capacity of V40Ti26Cr26Fe8 (particle size: ~325 μm) but improved the absorption reversibility, regardless of the oil viscosity. It was observed that silicone oil formed a thin oil layer of 320~460 nm in thickness on the surface of the V40Ti26Cr26Fe8 particles, which might improve the fluidity of the powder, reduce the self-locking phenomenon and alleviate the stress concentration on the container walls. Consequently, the maximum strain on the surface of the hydrogen storage container decreased by ≥22.5% after adding 5 wt% silicone oil with a viscosity of 1000 cSt.

Cellular senescence (CS) is closely related to tumor progression. However, the studies about CS genes across human cancers have not explored the relationship between cancer senescence signature and telomere length. Additionally, single-cell analyses have not revealed the evolutionary trends of malignant cells and immune cells at the CS level. We defined a CS-associated signature, called “senescence signature”, and found that patients with higher senescence signature had worse prognosis. Higher senescence signature was related to older age, higher genomic instability, longer telomeres, increased lymphocytic infiltration, higher pro-tumor immune infiltrates (Treg cells and MDSCs), and could predict responses to immune checkpoint inhibitor therapy. Single-cell analysis further reveals malignant cells and immune cells share a consistent evolutionary trend at the CS level. MAPK signaling pathway and apoptotic processes may play a key role in CS, and senescence signature may effectively predict sensitivity of MEK1/2 inhibitors, ERK1/2 inhibitors and BCL-2 family inhibitors. We also developed a new CS prediction model of cancer survival and established a portal website to apply this model ( https://bio-pub.shinyapps.io/cs_nomo/ ).

Background Multi‐omics features of cell‐free DNA (cfDNA) can effectively improve the performance of non‐invasive early diagnosis and prognosis of cancer. However, multimodal characterization of cfDNA remains technically challenging. Methods We developed a comprehensive multi‐omics solution (COMOS) to specifically obtain an extensive fragmentomics landscape, presented by breakpoint characteristics of nucleosomes, CpG islands, DNase clusters and enhancers, besides typical methylation, copy number alteration of cfDNA. The COMOS was tested on 214 plasma samples of diffuse large B‐cell lymphoma (DLBCL) and matched healthy controls. Results For early diagnosis, COMOS improved the area under the curve (AUC) value to .993 compared with the individual omics model, with a sensitivity of 95% at 98% specificity. Detection sensitivity achieved 91% at 99% specificity in early‐stage patients, while the AUC values of the individual omics model were 0.942, 0.968, 0.989, 0.935, 0.921, 0.781 and 0.917, respectively, with lower sensitivity and specificity. In the treatment response cohort, COMOS yielded a superior sensitivity of 88% at 86% specificity (AUC, 0.903). COMOS has achieved excellent performance in early diagnosis and treatment response prediction. Conclusions Our study provides an effectively improved approach with high accuracy for the diagnosis and prognosis of DLBCL, showing great potential for future clinical application. Key points A comprehensive multi‐omics solution to specifically obtain an extensive fragmentomics landscape, presented by breakpoint characteristics of nucleosomes, CpG islands, DNase clusters and enhancers, besides typical methylation, copy number alteration of cfDNA. Integrated model of cfDNA multi‐omics could be used for non‐invasive early diagnosis of DLBCL. Integrated model of cfDNA multi‐omics could effectively evaluate the efficacy of R‐CHOP before DLBCL treatment. A comprehensive multi‐omics solution capable of obtaining fragmentomics landscape of cfDNA was developed and followed by an integrated model for non‐invasive early diagnosis of DLBCL, achieving excellent efficacy of R‐CHOP before DLBCL treatment.

Three years into the SARS-CoV-2 pandemic, the virus continues to mutate despite widespread vaccination, posing ongoing challenges for epidemic prevention and control. The relationship between viral shedding and immune escape remains under investigation. This study aims to examine the association between viral shedding and immune escape in the BA.4/5 and BF.7 variants. We included 542 patients infected with the Omicron variant from Beijing Xiaotangshan shelter hospital. Based on the viral strain, patients were divided into BA.4/5 group and BF.7 group. Additionally, we categorized patients into rapid viral shedding and slow viral shedding groups according to their viral shedding rates. We explored the relationship between viral shedding and immune-related clinical indicators during this period. Of the 542 patients, 118 were infected with BA.4/5 variant, and 424 were infected with BF.7 variant. The viral shedding duration differed significantly between BA.4/5 and BF.7 groups (  < 0.0001). However, there was no statistically significant correlation between viral shedding duration and immune-related indicators, such as WBC, Hb, PLT, Neu, Lym, CRP, allergy, fever, and vaccination status (  > 0.05). Furthermore, viral shedding duration was not associated with vaccination status, intervals between vaccinations, or vaccine types (  > 0.05). The duration of viral shedding in patients infected with Omicron variants BA.4/5 and BF.7 is not associated with WBC, Hb, Lym, CRP, fever, allergy, or vaccine-related indicators. This lack of association may be attributed to immune escape mechanisms.