To examine haemoglobin level and anaemia status among infants under six months of age in rural China.

A cross-sectional survey collected data among infants under six months and their primary caregivers in Sichuan, China. Anaemia was defined using both the WHO and China Pediatrics Association thresholds. Multivariable linear regression was used to identify relevant factors among two age groups (<4 months; 4-5 months).

80 townships were selected in Sichuan, China from November to December 2019.

942 infants under six months, while haemoglobin level were tested for 577 infants.

The overall mean (±SD) haemoglobin level was 106.03 (± 12.04) g/L. About 62.6% (95%CI: 58.5, 66.6) of sample infants were anemic using the WHO threshold, and 20.5% (95%CI: 17.3, 24.1) were anemic using the China Pediatrics Association thresholds. Anaemia rates rose with increasing age in months. Multivariable linear regressions revealed that lower haemoglobin levels were significantly associated with lower birth weight (<4 months: b = 4.14, 95% CI: 0.19, 8.08; 4-5 months: b = 6.60, 95% CI: 2.94, 10.27) and delivery by cesarean section (<4 months: b = -4.64, 95%CI: -7.79, -1.49; 4-5 months: b = -4.58, 95%CI: -7.45, -1.71).

A large share of infants under six months in rural western China are anemic. Infants with low-birth-weight and cesarean-delivered should be prioritized for anaemia testing. Future studies should move the point of focus forward to at least 4 months of age and examine the link between cesarean-section and anaemia to promote health and development in infancy.

Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.

To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.

We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.

We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.

These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

Based on the development of telemedicine and the experience of using it during the COVID-19 epidemic, we aimed to explore its convenience and shortcomings to provide a reference for the further improvement of telemedicine.

Traditional healthcare has been significantly affected by the outbreak of COVID-19, which has increased fear in patients with chronic diseases and increased the difficulty of obtaining hospitalized treatment.

This is a conceptual article. The literature search is based on Pubmed, including articles published between January 2015 and December 2020. The purpose was to determine whether telemedicine is effective in the management of chronic diseases in the epidemic situation and to develop telemedicine and chronic disease management for long-term epidemic situations in the future.

Telemedicine has demonstrated its advantages during the COVID-19 epidemic and can provide diversified clinical care services for patients with chronic diseases; these services have played a vital role in epidemic prevention and control, greatly alleviated the shortage of medical resources, increased the utilization level of medical resources, and reduced the cross-infection risk during treatment in hospitals. Furthermore, the epidemic situation presents opportunities for the development of diagnosis and treatment methods via the internet and active health management modalities.

Understanding the extent of aerosol-based transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is important for tailoring interventions for control of the coronavirus disease 2019 (COVID-19) pandemic. Multiple studies have reported the detection of SARS-CoV-2 nucleic acid in air samples, but only one study has successfully recovered viable virus, although it is limited by its small sample size.

We aimed to determine the extent of shedding of viable SARS-CoV-2 in respiratory aerosols from COVID-19 patients.

In this observational air sampling study, air samples from airborne-infection isolation rooms (AIIRs) and a community isolation facility (CIF) housing COVID-19 patients were collected using a water vapor condensation method into liquid collection media. Samples were tested for presence of SARS-CoV-2 nucleic acid using quantitative real-time polymerase chain reaction (qRT-PCR), and qRT-PCR-positive samples were tested for viability using viral culture.

Samples from 6 (50%) of the 12 sampling cycles in hospital rooms were positive for SARS-CoV-2 RNA, including aerosols ranging from <1 µm to >4 µm in diameter. Of 9 samples from the CIF, 1 was positive via qRT-PCR. Viral RNA concentrations ranged from 179 to 2,738 ORF1ab gene copies per cubic meter of air. Virus cultures were negative after 4 blind passages.

Although SARS-CoV-2 is readily captured in aerosols, virus culture remains challenging despite optimized sampling methodologies to preserve virus viability. Further studies on aerosol-based transmission and control of SARS-CoV-2 are needed.

This study assesses the prevalence of childhood undernutrition from 2001 to 2016 and estimate projections of undernutrition for 2016–2030 in Nepal.

The study used data from four rounds of a cross-sectional survey of Nepal Demographic and Health Survey (NDHS) conducted in 2001, 2006, 2011 and 2016. Descriptive analyses were conducted to calculate prevalence, binary logistic regression was used to test the significance of trends over time and autoregressive integrated moving average model was used to forecast the prevalence of childhood undernutrition.

The children and household member datasets from four NDHS were merged to assess the trends of childhood undernutrition in Nepal.

A total of 16 613 children (8399 male and 8214 female) under 5 years of age were selected for anthropometric measurements using a stratified cluster random sampling method.

Overall results show a decline in prevalence of stunting from 57·2 % to 35·8 % (P < 0·001), underweight from 42·7 % to 27 % (P < 0·001) and wasting from 11·2 % to 9·7 % (P < 0·05) from 2001 to 2016. However, different population subgroups have a higher prevalence of undernutrition than national average. Further, the analyses show that the prevalence of stunting will decline to 14·3 % and wasting to 8·4 % by 2030.

A remarkable decrease in the prevalence of stunting and underweight has been observed over the last 15 years. Nepal is likely to achieve the nutritional targets for stunting but not for wasting by 2030. Given large subpopulation variations, further improvement in undernutrition require more specific, targeted and localised programmes.

The risk of environmental contamination by severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the intensive care unit (ICU) is unclear. We evaluated the extent of environmental contamination in the ICU and correlated this with patient and disease factors, including the impact of different ventilatory modalities.

In this observational study, surface environmental samples collected from ICU patient rooms and common areas were tested for SARS-CoV-2 by polymerase chain reaction (PCR). Select samples from the common area were tested by cell culture. Clinical data were collected and correlated to the presence of environmental contamination. Results were compared to historical data from a previous study in general wards.

In total, 200 samples from 20 patient rooms and 75 samples from common areas and the staff pantry were tested. The results showed that 14 rooms had at least 1 site contaminated, with an overall contamination rate of 14% (28 of 200 samples). Environmental contamination was not associated with day of illness, ventilatory mode, aerosol-generating procedures, or viral load. The frequency of environmental contamination was lower in the ICU than in general ward rooms. Eight samples from the common area were positive, though all were negative on cell culture.

Environmental contamination in the ICU was lower than in the general wards. The use of mechanical ventilation or high-flow nasal oxygen was not associated with greater surface contamination, supporting their use and safety from an infection control perspective. Transmission risk via environmental surfaces in the ICUs is likely to be low. Nonetheless, infection control practices should be strictly reinforced, and transmission risk via droplet or airborne spread remains.

Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function.

Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks.

At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions.

These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.