Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. Methods Patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. Results A total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

[摘 要] 目的：探讨圣草次苷对食管癌KYSE30细胞增殖的影响，并基于铁死亡探讨其可能的作用机制。方法：将食管癌KYSE30细胞分为8组：对照组（常规培养）、RSL3组（用3 μmol/L铁死亡诱导剂RSL3处理）、圣草次苷组（用75 μmol/L圣草次苷处理）、圣草次苷 + Fer-1组（用5 μmol/L铁死亡抑制剂Fer-1 + 75 μmol/L圣草次苷处理）、Fer-1组（用5 μmol/L Fer-1处理）、oe-NC组（转染空白载体对照）、oe-STAT3组（转染STAT3过表达载体）、oe-STAT3 + 圣草次苷组（转染STAT3过表达载体后，用75 μmol/L圣草次苷处理）。采用CCK-8法、EdU掺入实验和克隆形成实验分别检测各组细胞的增殖能力，ELISA检测细胞内铁死亡相关指标的水平，WB法检测STAT3/GPX4通路相关蛋白的表达。构建KYSE30细胞裸鼠皮下移植瘤模型，观察圣草次苷及Fer-1对移植瘤生长的影响。结果：圣草次苷能够抑制KYSE30细胞增殖和克隆形成，增加活性氧（ROS）、丙二醛（MDA）、Fe2+水平，降低谷胱甘肽（GSH）水平（均P < 0.05），并抑制裸鼠移植瘤的生长，这些作用能够被Fer-1所逆转（P < 0.05）。STAT3过表达可消除圣草次苷对铁死亡的诱导效应及其对STAT3/GPX4通路的抑制作用（P < 0.05）。结论：圣草次苷能够通过抑制STAT3/GPX4信号通路来诱导食管癌KYSE30细胞铁死亡，从而在食管癌中发挥显著的抗肿瘤效应。

Background: A 2018 study on the global burden of disease, accidents, and risk factors reported that 1.6 million peo ple died in 2017 due to household air pollution. Poor indoor air quality has been highlighted as a contributing factor to respiratory diseases, cardiovascular conditions, and exacerbation of asthma and allergies. A 2019 study estimated that long-term exposure to fine particulate matter (PM2.5) with a diameter of 2.5 micrometers or less reduces average life expectancy by 1.8 years, with more severe effects in highly polluted regions. Additionally, a study by Miller et al. (2007) found that prolonged exposure to PM2.5 increases the risk of cardiovascular diseases, particularly among women. Direct measurement devices are highly effective in determining indoor PM2.5 concentrations, identifying sources of pollution, tracking pollutant dispersion, and monitoring temporal variations. Studies suggest that direct measurement is an accurate, cost-effective method that provides detailed data suitable for local conditions. Aim: To investigate the indoor air quality of houses and apartments in Darkhan city during the winter season using the Purple Air monitoring device. Materials and Methods: A cross-sectional study was conducted with a targeted sample of 128 households in Darkhan city. The study examined factors such as stove type, type of coal used, annual and daily coal consumption, frequency of heating, and chimney sealing conditions. To collect data, the Purple Air monitoring device was installed in each house hold for a month, after which it was retrieved. During retrieval, participants completed a questionnaire. The questionnaire consisted of 55 questions across 7 pages at the time of device installation and 25 questions across 3 pages at the time of device retrieval. The collected data was analyzed using SPSS 25.0. Results: A total of 128 households in Darkhan city participated in the study. The average duration of residence in the current home was 9.5 years, with no statistically significant variation. The distribution of housing types was as follows: traditional Mongolian gers (40.6%), houses (39.1%), and apartments (20.3%). The 24-hour average PM2.5 concentration was highest in gers (70.9 μg/m³), followed by houses (46.8 μg/m³) and apartments (22.8 μg/m³), with a statistically significant difference (p=0.0001). PM2.5 levels were most variable in gers, followed by houses and then apartments. House holds using central heating (apartments) had an average 24-hour PM2.5 concentration of 22.8 μg/m³, whereas households using stoves (gers and houses) had a significantly higher concentration of 59.4 μg/m³ (p=0.0001). However, there was no statistically significant difference between traditional and improved stoves. Among study participants, 21.4% reported that someone in their household smoked indoors. Additionally, 86.5% regularly burned incense, candles, or herbs, while 99.2% did not use an air purifier. Conclusion The indoor particulate matter concentration in houses and gers in Darkhan was 59.4μг/m3. Variations in stove types, poor chimney sealing limited space, and frequent gaps and cracks contribute to increased spread of indoor air pollutants.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and pathological brain changes. While aging is the primary risk factor, circadian rhythm disruption (CRD) is increasingly recognized as a central driver of AD pathology. CRD exacerbates oxidative stress, systemic inflammation, and gut microbiome dysbiosis, impairing sleep-wake cycles, disrupting metabolic homeostasis, and promoting neuroinflammation, ultimately accelerating disease progression. Oxidative stress, a key factor in neuronal damage, is both a cause and consequence of circadian misalignment, while mitochondrial dysfunction further amplifies oxidative damage, impairing synaptic function and cognitive stability. Additionally, gut microbiome dysbiosis contributes to neuroinflammatory processes, worsening neurodegeneration. Given these complex interactions, this review aims to elucidate the role of CRD in AD pathology and explore potential therapeutic interventions targeting circadian dysfunction. Specifically, it examines the efficacy of time-restricted feeding (TRF), a dietary strategy that aligns food intake with circadian rhythms. TRF has shown promise in restoring circadian function, reducing oxidative stress, improving mitochondrial health, and promoting gut microbiome diversity. By addressing CRD, TRF may offer a novel approach to mitigating AD pathologies. This review also identifies current research gaps and future directions for developing circadian-based interventions in AD prevention and treatment.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.