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.

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.

Purinergic P2Y Receptor Antagonists ; Consensus ; Cardiovascular System ; Cardiology ; Asia

OBJECTIVE: To analyze the distribution characteristics of emerging and reemerging Oncomelania hupensis snails after the criteria for transmission control of schistosomiasis were achieved in China, so as to provide insights into assessment of schistosomiasis transmission risk and formulation of snail control strategies during the elimination phase. METHODS: O. hupensis survey data in China from 2015 to 2021 were collected from the National Schistosomiasis Pevention and Control Information Management System, and the distribution characteristics of emerging and reemerging O. hupensis snails were descriptively analyzed. RESULTS: Emerging and reemerging O. hupensis snails were identified in China each year from 2015 to 2021, with relatively larger areas with emerging and reemerging O. hupensis snail habitats in 2016 and 2021, and relatively higher numbers of counties (districts) where emerging and reemerging O. hupensis snails were detected in 2016 and 2021. A total of 4 586.30 hm² of emerging O. hupensis snail habitats were found in 10 schistosomiasis-endemic provinces of China (except Fujian and Yunnan Provinces) from 2015 to 2021, with 96.80% in Anhui, Hunan and Hubei provinces, where marshland and lake endemic foci were predominant. A total of 21 023.90 hm² of reemerging O. hupensis snail habitats were found in 12 schistosomiasis-endemic provinces of China from 2015 to 2021, with 97.67% in six provinces of Hubei, Sichuan, Jiangxi, Jiangsu, Yunnan and Anhui, where marshland and lake and hilly endemic regions were predominant. Emerging snail habitats were found in 15.08% of all schistosomiasisendemic counties (districts) in China from 2015 to 2021, and 78.75% of all emerging snail habitats were identified in 11 schistosomiasis-endemic counties (districts), with the largest area of emerging snail habitats found in Lixian County, Hunan Province (645.00 hm²). Reemerging snail habitats were found in 47.67% of all schistosomiasis-endemic counties (districts) in China from 2015 to 2021, and 43.29% of all reemerging snail habitats were identified in 11 schistosomiasis-endemic counties (districts), with the largest area of reemerging snail habitats found in Weishan Li and Hui Autonomous County of Hunan Province (1 579.70 hm²). CONCLUSIONS Emerging and reemerging O. hupensis snails were identified in China each year from 2015 to 2021, with much larger areas of reemerging snail habitats than emerging snail habitats, and larger numbers of schistosomiasis-endemic provinces and counties (districts) with reemerging snails were found that those of provinces and counties (districts) with emerging snails. Specific snail control interventions are required tailored to the causes of emerging and reemerging snail habitats. Both emergence and reemergence of O. hupensis snails should be paid attention to in marshland and lake endemic areas, and Guangxi Zhuang Autonomous Region, Shanghai Municipality and Zhejiang Province where schistosomiasis had been eliminated, and reemergence of O. hupensis snails should be given a high priority in hilly areas. In addition, monitoring of O. hupensis snails should be reinforced in snail-free areas after flooding.
Humans ; China/epidemiology* ; Schistosomiasis/prevention & control* ; Cities ; Ecosystem ; Lakes

OBJECTIVE: To establish a snail control approach for spraying chemicals with drones against Oncomelania hupensis in complex snail habitats in hilly regions, and to evaluate its molluscicidal effect. METHODS: The protocol for evaluating the activity of spraying chemical molluscicides with drones against O. hupensis snails was formulated based on expert consultation and literature review. In August 2022, a pretest was conducted in a hillside field environment (12 000 m²) north of Dafengji Village, Dacang Township, Weishan County, Yunnan Province, which was assigned into four groups, of no less than 3 000 m² in each group. In Group A, environmental cleaning was not conducted and 5% niclosamide ethanolamine salt granules were sprayed with drones at a dose of 40 g/m², and in Group B, environmental cleaning was performed, followed by 5% niclosamide ethanolamine salt granules sprayed with drones at a dose of 40 g/m², while in Group C, environmental cleaning was not conducted and 5% niclosamide ethanolamine salt granules were sprayed with knapsack sprayers at a dose of 40 g/m², and in Group D, environmental cleaning was performed, followed by 5% niclosamide ethanolamine salt granules sprayed with knapsack sprayers at a dose of 40 g/m². Then, each group was equally divided into six sections according to land area, with Section 1 for baseline surveys and sections 2 to 6 for snail surveys after chemical treatment. Snail surveys were conducted prior to chemical treatment and 1, 3, 5, 7 days post-treatment, and the mortality and corrected mortality of snails, density of living snails and costs of molluscicidal treatment were calculated in each group. RESULTS: The mortality and corrected mortality of snails were 69.49%, 69.09%, 53.57% and 83.48%, and 68.58%, 68.17%, 52.19% and 82.99% in groups A, B, C and D 14 days post-treatment, and the density of living snails reduced by 58.40%, 63.94%, 68.91% and 83.25% 14 days post-treatment relative to pre-treatment in four groups, respectively. The median concentrations of chemical molluscicides were 37.08, 35.42, 42.50 g/m² and 56.25 g/m² in groups A, B, C and D, and the gross costs of chemical treatment were 0.93, 1.50, 0.46 Yuan per m² and 1.03 Yuan per m² in groups A, B, C and D, respectively. CONCLUSIONS The molluscicidal effect of spraying 5% niclosamide ethanolamine salt granules with drones against O. hupensis snails is superior to manual chemical treatment without environmental cleaning, and chemical treatment with drones and manual chemical treatment show comparable molluscicidal effects following environmental cleaning in hilly regions. The cost of chemical treatment with drones is slightly higher than manual chemical treatment regardless of environmental cleaning. Spraying 5% niclosamide ethanolamine salt granules with drones is recommended in complex settings with difficulty in environmental cleaning to improve the molluscicidal activity and efficiency against O. hupensis snails.
Niclosamide/pharmacology* ; Ethanolamine/pharmacology* ; Unmanned Aerial Devices ; China ; Molluscacides/pharmacology* ; Ethanolamines