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.

Background: According to the World Health Organization (WHO), 6.7 million people die annually due to air pollution caused by solid fuel use, with the majority of deaths resulting from respiratory diseases and cardiovascular conditions. In Mongolia, air pollution ranks as the fourth leading risk factor contributing to mortality, following hypertension, diabetes, and other major health risks. Although there have been numerous studies on outdoor air pollution in Mongolia, research linking indoor air pollution at the household level with the health status of residents remains limited. Aim: To compare indoor PM2.5 concentrations in households of Ulaanbaatar and Darkhan and examine their association with hypertension during the winter season. Materials and Methods: The study was conducted during November and December 2023, and January 2024, involving 240 households in Ulaanbaatar and Darkhan. Indoor PM2.5 concentrations were measured using Purple Air real-time sensors continuously for 24 hours over approximately one month. After measuring indoor air pollution, individuals aged 18–60 years living in the selected households were recruited based on specific inclusion criteria. Blood pressure was measured three times and the average value was recorded. Information on respiratory illnesses was collected through structured questionnaires. Statistical analysis was performed using STATA version 19.0. Results: A total of 241 households participated in the study, with 116 from Ulaanbaatar and 125 from Darkhan. Of the participants, 46.5% were male and 53.5% were female. In terms of housing type, 96 households (39.8%) lived in gers, 97 (40.2%) lived in stove-heated houses, and 48 (19.9%) lived in apartments. Among all participants, 66.0% (n=159) had hypertension and 34.0% (n=79) had normal blood pressure. Among participants aged over 40, 69.9–88.5% had hypertension, which is statistically significantly higher compared to younger individuals (p=0.0001). By body mass index, 75.3% (n=72) of overweight individuals and 78.4% (n=58) of obese participants had hypertension, showing a statistically significant difference compared to participants with normal weight (p=0.0001). The 24-hour average concentration of indoor PM2.5 was measured using the Purple Air device, and the levels in gers and stove-heated houses exceeded the limit set by the MNS 4585:2025 standard (37.5 µg/m³) Conclusion This study identified a relationship between environmental factors, such as air pollution and housing type, and the prevalence of hypertension. The indoor PM2.5 concentration in gers and stove-heated houses was above the standard limit, indicating a negative impact on the health of those residents. Furthermore, the high prevalence of hypertension among participants over the age of 40 and those who are overweight suggests a possible link to lifestyle and environmental conditions.

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.

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.

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.