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.

Background: Particulate matter with an aerodynamic diameter of 2.5 micrometers or smaller (PM2.5) penetrates deep into the alveoli through the respiratory tract and is characterized by its ability to induce oxidative stress, systemic inflammation, and vascular inflammation. Mongolia ranks among the countries with the highest levels of air pollution. In Ulaanbaatar, where more than half of the country’s population resides, wintertime PM2.5 concentrations often exceed 200 μg/m³, which is about eight times higher than the World Health Organization (WHO) guideline value. A study involving 1,200 adults in Ulaanbaatar showed that quality of life deteriorated sharply during periods of high air pollution, with effects more pronounced among individuals who already had impaired respiratory function. Aim: To examine the relationship between indoor household PM2.5 concentrations and lung function indicators among adults in Ulaanbaatar and Darkhan. Materials and Methods: This analytical cross-sectional study recruited adult participants from Ulaanbaatar and Darkhan through targeted sampling. Household air quality was measured using PurpleAir sensors, which were installed in participants’ homes for one month. After exposure measurement, lung function was assessed via spirometry. Statistical analyses were conducted using SPSS version 25.0. Results: A total of 236 participants were included: 114 (48.3%) from Ulaanbaatar and 122 (51.7%) from Darkhan. The sample consisted of 111 men (47.0%) and 125 women (53.0%). The mean indoor PM2.5 concentration was 66.24 μg/m³ (SD 44.87 μg/m³), ranging from a minimum of 7.79 μg/m³ to a maximum of 264.55 μg/m³. Stratification by housing type showed the highest PM2.5 levels in gers (82.34 μg/m³), followed by detached houses (67.34 μg/m³), while apartments had the lowest concentrations (32.24 μg/m³). Correlation analysis revealed statistically significant negative associations between PM2.5 levels and measures of expiratory function, including the FEV1/FVC ratio, peak expiratory flow (PEF), and mid-expiratory flow (FEF25–75). Reduced forced vital capacity (FVC) was observed in 9.4% of participants, reduced forced expiratory volume in one second (FEV1) in 15.3%, and a decreased FEV1/FVC ratio in 3.8%. Conclusion Indoor household PM2.5 concentrations were highest in gers, and expiratory flow-related lung function parameters showed significant negative associations with particulate exposure. This suggests that indoor PM2.5 primarily affects airflow limitation rather than overall lung volumes in this population.

BACKGROUND: The FAVOR (Comparison of Quantitative Flow Ratio Guided and Angiography Guided Percutaneous Intervention in Patients with Coronary Artery Disease) III China trial demonstrated that percutaneous coronary intervention (PCI) lesion selection using quantitative flow ratio (QFR) measurement, a novel angiography-based approach for estimating fractional flow reserve, improved two-year clinical outcomes compared with standard angiography guidance. This study aimed to assess the cost-effectiveness of QFR-guided PCI from the perspective of the current Chinese healthcare system. METHODS: This study is a pre-specified analysis of the FAVOR III China trial, which included 3825 patients randomized between December 25, 2018, and January 19, 2020, from 26 centers in China. Patients with stable or unstable angina pectoris or those ≥72 hours post-myocardial infarction who had at least one lesion with a diameter stenosis between 50% and 90% in a coronary artery with a ≥2.5 mm reference vessel diameter by visual assessment were randomized to a QFR-guided strategy or an angiography-guided strategy with 1:1 ratio. During the two-year follow-up, data were collected on clinical outcomes, quality-adjusted life-years (QALYs), estimated costs of index procedure hospitalization, outpatient cardiovascular medication use, and rehospitalization due to major adverse cardiac and cerebrovascular events (MACCE). The primary analysis calculated the incremental cost-effectiveness ratio (ICER) as the cost per MACCE avoided. An ICER of ¥10,000/MACCE event avoided was considered economically attractive in China. RESULTS: At two years, the QFR-guided group demonstrated a reduced rate of MACCE compared to the angiography-guided group (10.8% vs . 14.7%, P <0.01). Total two-year costs were similar between the groups (¥50,803 ± 21,121 vs . ¥50,685 ± 23,495, P = 0.87). The ICER for the QFR-guided strategy was ¥3055 per MACCE avoided, and the probability of QFR being economically attractive was 64% at a willingness-to-pay threshold of ¥10,000/MACCE avoided. Sensitivity analysis showed that QFR-guided PCI would become cost-saving if the cost of QFR were below ¥3682 (current cost: ¥3800). Cost-utility analysis yielded an ICER of ¥56,163 per QALY gained, with a 53% probability of being cost-effective at a willingness-to-pay threshold of ¥85,000 per QALY gained. CONCLUSION: In patients undergoing PCI, a QFR-guided strategy appears economically attractive compared to angiographic guidance from the perspective of the Chinese healthcare system. TRIAL REGISTRATION ClinicalTrials.gov , NCT03656848.
Humans ; Cost-Benefit Analysis ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Angiography/methods* ; Middle Aged ; Aged ; Coronary Artery Disease/surgery* ; Quality-Adjusted Life Years ; Fractional Flow Reserve, Myocardial/physiology*

Stearoyl-coenzyme A desaturase 1 (SCD1) catalyzes the rate-limiting step of de novo lipogenesis and modulates lipid homeostasis. Although numerous SCD1 inhibitors were tested for treating metabolic disorders both in preclinical and clinic studies, the tissue-specific roles of SCD1 in modulating obesity-associated metabolic disorders and determining the pharmacological effect of chemical SCD1 inhibition remain unclear. Here a novel role for intestinal SCD1 in obesity-associated metabolic disorders was uncovered. Intestinal SCD1 was found to be induced during obesity progression both in humans and mice. Intestine-specific, but not liver-specific, SCD1 deficiency reduced obesity and hepatic steatosis. A939572, an SCD1-specific inhibitor, ameliorated obesity and hepatic steatosis dependent on intestinal, but not hepatic, SCD1. Mechanistically, intestinal SCD1 deficiency impeded obesity-induced oxidative stress through its novel function of inducing metallothionein 1 in intestinal epithelial cells. These results suggest that intestinal SCD1 could be a viable target that underlies the pharmacological effect of chemical SCD1 inhibition in the treatment of obesity-associated metabolic disorders.