Objective To study the five-year survival status and influencing factors of elderly patients with lung cancer complicated with chronic obstructive pulmonary disease (COPD). Methods A cohort study was conducted to follow up 450 patients with lung cancer and chronic obstructive pulmonary disease who were hospitalized in our hospital from January 2018 to December 2023. The endpoint of the follow-up was the end of a five-year period or death. The Life Tables method was used to calculate survival rates and plot survival curves. The Cox proportional hazards model was used to analyze the influencing factors of five-year survival. Results The results indicated that the overall five-year survival rate of patients was 4.89%, and it decreased year by year. Cox regression analysis showed that age, gender, family functioning, and psychological status significantly influenced patient survival rate (all P<0.05). Stratified analysis found that the smoking status, family functioning, and psychological status of male patients all had an impact on survival rate (all P<0.05), while the psychological status of female patients had a more significant impact on survival (P=0.008). Conclusion This study provides a scientific basis for comprehensive intervention of elderly lung cancer patients with COPD. It is recommended that clinical attention should be paid to psychological and family factors to improve patient prognosis.

Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

OBJECTIVE To optimize the management model of drugs used in investigator-initiated trial （IIT）. METHODS With benchmarking analysis， based on the practical work experience of a tertiary specialized hospital in the field of IIT drug management in Shanghai， a thorough review was conducted， involving relevant laws， regulations， and academic literature to establish benchmark criteria and the evaluation standards. Starting from the initiation of IIT projects， a detailed comparative analysis of key processes was carried out， such as the receipt， storage， distribution， use and recycling of drugs for trial. The deficiencies in the current management of IIT drugs were reviewed in detail and a series of optimization suggestions were put forward. RESULTS It was found that the authorized records of drug management were missing， the training before project implementation was insufficient， and the records of receipt and acceptance of IIT drugs were incomplete. In light of these existing problems， improvement measures were put forward， including strengthening the training of drug administrators and stipulating that only drug administrators with pharmacist qualifications be eligible to inspect and accept drugs， etc. The related systems were improved， and 17 key points of quality control for the management of IIT drugs were developed. CONCLUSIONS A preliminary IIT drug management system for medical institutions has been established， which helps to improve the institutional X2023076） framework of medical institutions in this field.

The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

As the number of patients with compromised immune function increases and fungal resistance develops, so does the risk of contracting deadly fungi in humans. Both fungi and humans are eukaryotes, so identifying unique targets for antifungal drug development is difficult. In addition, the existing antifungal drugs are limited by toxicity, drug interaction and drug resistance in practical application, which leads to the increasing incidence and fatal rate of fungal infections. Therefore, it is urgent to develop new antifungal drugs. The semi-synthetic technology using microbial fermentation products from natural sources as lead compounds has become the most used method in structural modification of antifungal drugs due to its advantages of few reaction steps and easy operation. This paper will introduce the current status of natural antifungal drugs in clinical use, as well as the latest progress in the research and development of new semi-synthetic antifungal drugs, and summarize their mechanism of action, structural modifications, advantages and disadvantages, so as to provide reference for the subsequent development of new antifungal drugs.

Fibrosis, the pathological scarring of vital organs, is a severe and often irreversible condition that leads to progressive organ dysfunction. It is particularly pronounced in organs like the liver, kidneys, lungs, and heart. Despite its clinical significance, the full understanding of its etiology and complex pathogenesis remains incomplete, posing substantial challenges to diagnosing, treating, and preventing the progression of fibrosis. Among the various molecular players involved, platelet-derived growth factor-C (PDGF-C) has emerged as a crucial factor in fibrotic diseases, contributing to the pathological transformation of tissues in several key organs. PDGF-C is a member of the PDGFs family of growth factors and is synthesized and secreted by various cell types, including fibroblasts, smooth muscle cells, and endothelial cells. It acts through both autocrine and paracrine mechanisms, exerting its biological effects by binding to and activating the PDGF receptors (PDGFRs), specifically PDGFRα and PDGFRβ. This binding triggers multiple intracellular signaling pathways, such as JAK/STAT, PI3K/AKT and Ras-MAPK pathways. which are integral to the regulation of cell proliferation, survival, migration, and fibrosis. Notably, PDGF-C has been shown to promote the proliferation and migration of fibroblasts, key effector cells in the fibrotic process, thus accelerating the accumulation of extracellular matrix components and the formation of fibrotic tissue. Numerous studies have documented an upregulation of PDGF-C expression in various fibrotic diseases, suggesting its significant role in the initiation and progression of fibrosis. For instance, in liver fibrosis, PDGF-C stimulates hepatic stellate cell activation, contributing to the excessive deposition of collagen and other extracellular matrix proteins. Similarly, in pulmonary fibrosis, PDGF-C enhances the migration of fibroblasts into the damaged areas of lungs, thereby worsening the pathological process. Such findings highlight the pivotal role of PDGF-C in fibrotic diseases and underscore its potential as a therapeutic target for these conditions. Given its central role in the pathogenesis of fibrosis, PDGF-C has become an attractive target for therapeutic intervention. Several studies have focused on developing inhibitors that block the PDGF-C/PDGFR signaling pathway. These inhibitors aim to reduce fibroblast activation, prevent the excessive accumulation of extracellular matrix components, and halt the progression of fibrosis. Preclinical studies have demonstrated the efficacy of such inhibitors in animal models of liver, kidney, and lung fibrosis, with promising results in reducing fibrotic lesions and improving organ function. Furthermore, several clinical inhibitors, such as Olaratumab and Seralutinib, are ongoing to assess the safety and efficacy of these inhibitors in human patients, offering hope for novel therapeutic options in the treatment of fibrotic diseases. In conclusion, PDGF-C plays a critical role in the development and progression of fibrosis in vital organs. Its ability to regulate fibroblast activity and influence key signaling pathways makes it a promising target for therapeutic strategies aiming at combating fibrosis. Ongoing research into the regulation of PDGF-C expression and the development of PDGF-C/PDGFR inhibitors holds the potential to offer new insights and approaches for the diagnosis, treatment, and prevention of fibrotic diseases. Ultimately, these efforts may lead to the development of more effective and targeted therapies that can mitigate the impact of fibrosis and improve patient outcomes.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.