Bioscience trends最新文献

Alzheimer's disease (AD), the leading cause of dementia, significantly impacts global public health, with cases expected to exceed 150 million by 2050. Late-onset Alzheimer's disease (LOAD), predominantly influenced by the APOE-ε4 allele, exhibits complex pathogenesis involving amyloid-β (Aβ) plaques, neurofibrillary tangles (NFTs), neuroinflammation, and blood-brain barrier (BBB) disruption. Proteomics has emerged as a pivotal technology in uncovering molecular mechanisms and identifying biomarkers for early diagnosis and intervention in AD. This paper reviews the genetic and molecular roles of APOE-ε4 in the pathology of AD, including its effects on Aβ aggregation, tau phosphorylation, neuroinflammation, and BBB integrity. Additionally, it highlights recent advances in serum proteomics, revealing APOE-ε4-dependent and independent protein signatures with potential as early biomarkers for AD. Despite technological progress, challenges such as population diversity, standardization, and distinguishing AD-specific biomarkers remain. Directions for future research emphasize multicenter longitudinal studies, multi-omics integration, and the clinical translation of proteomic findings to enable early detection of AD and personalized treatment strategies. Proteomics advances in AD research hold the promise of improving patient outcomes and reducing the global disease burden.

Lymph node metastasis in intrahepatic cholangiocarcinoma significantly impacts overall survival, emphasizing the need for a predictive model. This study involved patients who underwent curative liver resection between different time periods. Three machine learning models were constructed with a training cohort (2010-2016) and validated with a separate cohort (2019-2023). A total of 170 patients were included in the training set and 101 in the validation cohort. The lymph node status of patients not undergoing lymph node dissection was predicted, followed by survival analysis. Among the models, the support vector machine (SVM) had the best discrimination, with an area under the curve (AUC) of 0.705 for the training set and 0.754 for the validation set, compared to the random forest (AUC: 0.780/0.693) and the logistic regression (AUC: 0.703/0.736). Kaplan-Meier analysis indicated that patients in the positive lymph node group or predicted positive group had significantly worse overall survival (OS: p < 0.001 for both) and disease-free survival (DFS: p < 0.001 for both) compared to negative groups. An online user-friendly calculator based on the SVM model has been developed for practical application.

The prognosis for patients with hepatocellular carcinoma (HCC) depends on tumor stage and remnant liver function. However, it often includes tumor morphology, which is usually assessed with imaging studies or pathologic analysis, leading to limited predictive performance. Therefore, the aim of this study was to develop a simple and low-cost prognostic score for HCC based on serum biomarkers in routine clinical practice. A total of 3,100 patients were recruited. The least absolute shrinkage and selector operation (LASSO) algorithm was used to select the significant factors for overall survival. The prognostic score was devised based on multivariate Cox regression of the training cohort. Model performance was assessed by discrimination and calibration. Albumin (ALB), alkaline phosphatase (ALP), and alpha-fetoprotein (AFP) were selected by the LASSO algorithm. The three variables were incorporated into multivariate Cox regression to create the risk score (APP score = 0.390* ln (ALP) + 0.063* ln(AFP) - 0.033*ALB). The C-index, K-index, and time-dependent AUC of the score displayed significantly better predictive performance than 5 other models and 5 other staging systems. The model was able to stratify patients into three different risk groups. In conclusion, the APP score was developed to estimate survival probability and was used to stratify three strata with significantly different outcomes, outperforming other models in training and validation cohorts as well as different subgroups. This simple and low-cost model could help guide individualized follow-up.

In recent years, machine learning, and particularly deep learning, has shown remarkable potential in various fields, including medicine. Advanced techniques like convolutional neural networks and transformers have enabled high-performance predictions for complex problems, making machine learning a valuable tool in medical decision-making. From predicting postoperative complications to assessing disease risk, machine learning has been actively used to analyze patient data and assist healthcare professionals. However, the "black box" problem, wherein the internal workings of machine learning models are opaque and difficult to interpret, poses a significant challenge in medical applications. The lack of transparency may hinder trust and acceptance by clinicians and patients, making the development of explainable AI (XAI) techniques essential. XAI aims to provide both global and local explanations for machine learning models, offering insights into how predictions are made and which factors influence these outcomes. In this article, we explore various applications of machine learning in medicine, describe commonly used algorithms, and discuss explainable AI as a promising solution to enhance the interpretability of these models. By integrating explainability into machine learning, we aim to ensure its ethical and practical application in healthcare, ultimately improving patient outcomes and supporting personalized treatment strategies.

Recently, a systemic therapy for advanced hepatocellular carcinoma (HCC) has been developed. The regimen for unresectable HCC varies and includes single or multi-tyrosine kinase inhibitors, monoclonal antibodies, immune checkpoint inhibitors, or their combinations. Treatment with these agents begins with sorafenib as the first-line drug for unresectable HCC. Subsequently, several systemic therapies, including lenvatinib, ramucirumab, cabozantinib, and regorafenib have been investigated and established. With advances in systemic therapy for unresectable HCC, the prognosis of patients with unresectable HCC has improved significantly than previously. Conversion surgery, consisting of systemic therapy and surgery, showed the possibility of improving the prognosis than systemic therapy alone. Although a combination of atezolizumab and bevacizumab is mostly used for initially unresectable HCC to conduct conversion surgery because of the high response rate and fewer adverse events compared to others, many trials are being conducted to assess their efficacy for initially unresectable HCC. However, the appropriate timing of surgery and interval between systemic therapy and surgery remain controversial. To address these issues, a multidisciplinary team can play a vital role in determining the strategies for treating unresectable HCC. This review describes previous and current trends in the treatment of HCC, with a particular focus on conversion surgery for initially unresectable HCC.

This study aimed at analyzing and comparing the clinical efficacy and prognosis of repeat laparoscopic hepatectomy (r-LH) and radiofrequency ablation (RFA) in treating recurrent hepatocellular carcinoma (RHCC). Clinicopathological data of RHCC patients who underwent r-LH or RFA as treatment from three medical centers were retrospectively reviewed. Baseline characteristics at the recurrence time after initial hepatectomy and clinical outcomes following treatment of RHCC were compared between the two groups. Using the Kaplan-Meier method, survival curves for the two groups of patients were generated, and the log-rank test was used to compare survival differences. Propensity score matching (PSM) analysis was used to match patients of the r-LH and RFA groups in a 1:1 ratio. A total of 272 patients were enrolled, including 133 patients who underwent r-LH and 139 patients who received RFA. After PSM, 76 patients were matched in each study group. Compared with the r-LH group, the RFA group had shorter hospitalization and fewer postoperative complications. However, the r-LH group had significantly better overall survival (OS) and disease-free survival (DFS) than the RFA group before and after PSM. Subgroup analysis demonstrated that RHCC patients with solitary tumor or those with tumors located near the diaphragm, visceral surface or vessels, had survival benefits from r-LH. When tumor diameter ≤ 5 cm, r-LH appears to be an effective priority to RFA with a significantly higher OS and DFS rate in treating RHCC patients, especially for patients with solitary tumor and those with tumors located near the diaphragm, visceral surface or vessels.

The role of laparoscopic liver resection (LLR) for intrahepatic cholangiocarcinoma (ICC) remains debated. This study aimed to evaluate the short- and long-term outcomes of LLR vs. open liver resection (OLR) in ICC stratified by tumor burden score (TBS). ICC patients who underwent LLR or OLR were included from a multicenter database between July 2009 and October 2022. Patients were stratified into two cohorts based on whether the TBS was > 5.3. A 1:3 propensity score matching (PSM) analysis was performed between LLR and OLR in each cohort. Cox regression analysis was used to identify prognostic factors for ICC. A total of 626 patients were included in this study, 304 and 322 patients were classified into the low- and high-TBS groups, respectively. In the low-TBS group, after PSM, LLR was associated with less blood loss, lower CCI, fewer complications and shorter hospital stay (all p < 0.05). Kaplan-Meier curves revealed that LLR had better OS (p = 0.032). Multivariate Cox regression analysis showed that surgical procedure was an independent prognostic factor for ICC (HR: 0.445; 95% CI: 0.235-0.843; p = 0.013). In the high-TBS group, after PSM, LLR were associated with reduced blood loss, lower CCI, fewer complications and shorter hospital stay (all p < 0.05), while OS (p = 0.98) and DFS (p = 0.24) were similar between the two groups. TBS is an important prognostic factor for ICC. LLR is a safe and feasible option for ICC and leads to faster postoperative recovery. LLR can offer ICC a comparable and even better long-term prognosis than OLR.

Up to half of hepatocellular carcinoma (HCC) cases are diagnosed at an advanced stage, for which effective treatment options are lacking, resulting in a poor prognosis. Over the past few years, the combination of immune checkpoint inhibitors and anti-angiogenic targeted therapy has proven highly efficacious in treating advanced HCC, significantly extending patients' survival and providing a potential for sequential curative surgery. After sequential curative hepatectomy or liver transplantation following conversion therapy, patients can receive long-term survival benefits. In order to improve the long-term survival rate of the overall population with liver cancer and achieve the goal of a 15% increase in the overall 5-year survival rate outlined in the Healthy China 2030 blueprint, the Professional Committee for Prevention and Control of Hepatobiliary and Pancreatic Diseases of Chinese Preventive Medicine Association, Chinese Society of Liver Cancer, and the Liver Study Group of Surgery Committee of Beijing Medical Association organized in-depth discussions among relevant domestic experts in the field. These discussions focused on the latest progress since the release of the Chinese expert consensus on conversion therapy of immune checkpoint inhibitors combined antiangiogenic targeted drugs for advanced hepatocellular carcinoma (2021 Edition) and resulted in a new consensus on the modifications and supplements to related key points. This consensus aims to further guide clinical practice, standardize medical care, and promote the development of the discipline.

The prognosis following radical surgery for intrahepatic cholangiocarcinoma (ICC) is poor, and optimal follow-up strategies remain unclear, with ongoing debates regarding anatomic resection (AR) versus non-anatomic resection (NAR). This study included 680 patients from five hospitals, comparing a combination of eight feature screening methods and 11 machine learning algorithms to predict prognosis and construct integrated models. These models were assessed using nested cross-validation and various datasets, benchmarked against TNM stage and performance status. Evaluation metrics such as area under the curve (AUC) were applied. Prognostic models incorporating screened features showed superior performance compared to unselected models, with AR emerging as a key variable. Treatment recommendation models for surgical approaches, including DeepSurv, neural network multitask logistic regression (N-MTLR), and Kernel support vector machine (SVM), indicated that N-MTLR's recommendations were associated with survival benefits. Additionally, some patients identified as suitable for NAR were within groups previously considered for AR. In conclusion, three robust clinical models were developed to predict ICC prognosis and optimize surgical decisions, improving patient outcomes and supporting shared decision-making for patients and surgeons.

The current state of systemic therapy for advanced biliary tract cancer (BTC) has undergone significant changes. Currently, there are no clinical trials directly comparing various first-line systemic therapy regimens to each other, and these trials are unlikely to be conducted in the future. In this systematic review, after various abstracts and full-text articles published from the establishment of the database until October 2024 were searched, we included and analysed phase 3 clinical trials to evaluate the efficacy of different first-line systemic treatment regimens in advanced BTC. We used the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines and a random effects model to pool the overall effects. Finally, seven low-risk-of-bias trials (with all of the trials representing first-line trials) were included. A total of 4033 patients were included in seven first-line trials. In terms of progression-free survival (PFS), network meta-analysis revealed that durvalumab + gemcitabine + cisplatin (GemCis) triple therapy, S-1 + GemCis triple therapy, and pembrolizumab + GemCis triple therapy were superior to GemCis. In terms of overall survival (OS), network meta-analysis revealed that durvalumab + GemCis triple therapy and pembrolizumab + GemCis triple therapy outperformed GemCis. According to the ranking of the P scores, durvalumab + GemCis triple therapy ranked first in PFS and second in OS. Therefore, the advantages of molecular immunotherapy have gradually become known, which suggests that future trials should focus on other potential combinations and molecular immunotargeted therapies.