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Abstract Background The available evidence presented inconsistencies and inconclusive findings regarding the associations between co-existing asthma and mortality among COVID-19 patients. The objective of the current study is to investigate the relationship between asthma and severe outcomes after SARS-CoV-2 Omicron infection in an infection-naïve population. Methods A retrospective cohort study using propensity score matching was conducted. The COVID-19 patients requiring hospitalisation in Hong Kong from January 1, 2022, to November 13, 2022, an Omicron-predominated period, were identified. Severe clinical outcomes were defined as ICU admission and inpatient death after the first positive PCR results as well as a composite outcome of both. Results Of the 74,396 hospitalised COVID-19 patients admitted, 1,290 asthma patients and 18,641 non-asthma patients were included in the matched cohort. The rates of death and the composite outcome were 15·3% and 17·2%, respectively, among the non-asthma patients,12·2% and 13·6%, respectively, among the asthma patients, with adjusted hazard ratios equal to 0·775 (95% CI: 0·660–0·909) and 0·770 (95% CI: 0·662–0·895), respectively. The negative association was more apparent in the elderly and female groups. Asthma remained a factor that lowered the risk of disease severity even though the patients were not fully vaccinated with at least two doses. Conclusions We used real-world data to demonstrate that asthma was not a risk factor for COVID-19 severity of the infections of Omicron variant, even though the patients were not fully vaccinated.

Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation, from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.

Abstract Background As SARS-CoV-2 Omicron variants circulating globally since 2022, assessing the transmission characteristics, and the protection of vaccines against emerging Omicron variants among children and adolescents are needed for guiding the control and vaccination policies. Methods We conducted a retrospective cohort study for SARS-CoV-2 infections and close contacts aged

Background: Limited evidence exists regarding the association between ozone exposure and adverse sperm quality. We aimed to assess the association between ozone exposure and sperm quality, and identify susceptible exposure windows. Methods: We recruited 32,541 men aged between 22 and 65 years old attending an infertility clinic in Wuhan, Hubei Province, China from 2014 to 2020. Ozone data were obtained from a satellite-based spatiotemporal model. Generalized linear models were used to estimate the association between ozone exposure and sperm quality parameters, including sperm concentration, sperm count, sperm total motility, and sperm progressive motility during the entire stage of sperm development (0–90 days before ejaculation) and three crucial stages (0–9 days, 10–14 days and 70–90 days before ejaculation). Stratified analyses were performed to evaluate whether associations varied by age, body mass index, and education levels. Results: The final analysis included 27,854 adult men. A 10 μg/m3 increase in ozone concentrations during the entire stage of sperm development was associated with a −4.17 % (95 % CI: −4.78 %, −3.57 %) decrease in sperm concentration, −6.54 % (95 % CI: −8.03 %, −5.60 %) decrease in sperm count, −0.50 % (95 % CI: −0.66 %, −0.34 %) decrease in sperm total motility, and −0.07 % (95 % CI: −0.22 %, 0.09 %) decrease in sperm progressive motility. The associations were stronger during 70–90 days before ejaculation and among men with middle school and lower education for sperm concentration. Conclusions: Ozone exposure was associated with decreased sperm quality among Chinese adult men attending an infertility clinic. These results suggest that ozone may be a risk factor contributing to decreased sperm quality in Chinese men.

Objectives To estimate the association between the transition to daylight saving time and the risks of all cause and cause specific mortality in the US.Design Nationwide time series observational study based on weekly data.Setting US state level mortality data from the National Center for Health Statistics, with death counts from 50 US states and the District of Columbia, from the start of 2015 to the end of 2019.Population 13 912 837 reported deaths in the US.Main outcome measures Weekly counts of mortality for any cause, and for Alzheimer's disease, dementia, circulatory diseases, malignant neoplasms, and respiratory diseases.Results During the study period, 13 912 837 deaths were reported. The analysis found no evidence of an association between the transition to spring daylight saving time (when clocks are set forward by one hour on the second Sunday of March) and the risk of all cause mortality during the first eight weeks after the transition (rate ratio 1.003, 95% confidence interval 0.987 to 1.020). Autumn daylight saving time is defined in this study as the time when the clocks are set back by one hour (ie, return to standard time) on the first Sunday of November. Evidence indicating a substantial decrease in the risk of all cause mortality during the first eight weeks after the transition to autumn daylight saving time (0.974, 0.958 to 0.990). Overall, when considering the transition to both spring and autumn daylight saving time, no evidence of any effect of daylight saving time on all cause mortality was found (0.988, 0.972 to 1.005). These patterns of changes in mortality rates associated with transition to daylight saving time were consistent for Alzheimer's disease, dementia, circulatory diseases, malignant neoplasms, and respiratory diseases. The protective effect of the transition to autumn daylight saving time on the risk of mortality was more pronounced in elderly people aged ≥75 years, in the non-Hispanic white population, and in those residing in the eastern time zone.Conclusions In this study, transition to daylight saving time was found to affect mortality patterns in the US, but an association with additional deaths overall was not found. These findings might inform the ongoing debate on the policy of shifting daylight saving time.

Abstract Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.

We tested seroprevalence of open reading frame 8 antigens to infer the number of unrecognized SARS-CoV-2 Omicron infections in Hong Kong during 2022. We estimate 33.6% of the population was infected, 72.1% asymptomatically. Surveillance and control activities during large-scale outbreaks should account for potentially substantial undercounts.

In the post-COVID-19 pandemic era, influenza virus infections continuously lead to a global disease burden. Evaluating vaccine effectiveness against influenza infection is crucial to inform vaccine design and vaccination strategy. In this study, we recruited 1120 patients with influenza-like illness (ILI) who attended fever clinics of 4 sentinel hospitals in the Ili Kazakh Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China, from January 1 to April 7, 2024. Using a test-negative design, we estimated influenza vaccine effectiveness (VE) of 54.7% (95% CrI: 23.7, 73.1) against medical-attended influenza infection, with 62.3% (95% CrI: 29.3, 79.8) against influenza A, and 51.2% (95% CrI: 28.7, 83.0) against influenza B. Despite the moderate VE estimated in this study, influenza vaccination remains the most important approach to prevent influenza at the community level.

Background: While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people. Methods: Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures. Results: A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: −0.239 [95 % CI: −0.431 to −0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt. Conclusions: This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.

BackgroundOur previous multicenter case-control study showed that aging, up-regulation of platelet glycogen synthase kinase-3β (GSK-3β), impaired olfactory function, and ApoE ϵ4 genotype were associated with cognitive decline in type 2 diabetes mellitus (T2DM) patients. However, the causal relationship between these biomarkers and the development of cognitive decline in T2DM patients remains unclear.MethodsTo further investigate this potential relationship, we designed a 6-year follow-up study in 273 T2DM patients with normal cognitive in our previous study. Baseline characteristics of the study population were compared between T2DM patients with and without incident mild cognitive impairment (MCI). We utilized Cox proportional hazard regression models to assess the risk of cognitive impairment associated with various baseline biomarkers. Receiver operating characteristic curves (ROC) were performed to evaluate the diagnostic accuracy of these biomarkers in predicting cognitive impairment.ResultsDuring a median follow-up time of 6 years (with a range of 4 to 9 years), 40 patients (16.13%) with T2DM developed MCI. Participants who developed incident MCI were more likely to be older, have a lower education level, have more diabetic complications, a higher percentage of ApoE ϵ4 allele and a higher level of platelet GSK-3β activity (rGSK-3β) at baseline (P

Abstract Background Although COVID-19 vaccines and their booster regimens protect against symptomatic infections and severe outcomes, there is limited evidence about their protection against asymptomatic and symptomatic infections in real-world settings, particularly when considering that the majority of SARS-CoV-2 Omicron infections were asymptomatic. We aimed to assess the effectiveness of the booster dose of inactivated vaccines in mainland China, i.e., Sinopharm (BBIBP-CorV) and Sinovac (CoronaVac), against Omicron infection in an Omicron BA.5 seeded epidemic. Methods Based on an infection-naive but highly vaccinated population in Urumqi, China, the study cohort comprised all 37,628 adults who had a contact history with individuals having SARS-CoV-2 infections, i.e., close contacts, between August 1 and September 7, 2022. To actively detect SARS-CoV-2 infections, RT-PCR tests were performed by local authorities on a daily basis for all close contacts, and a testing-positive status was considered a laboratory-confirmed outcome. The cohort of close contacts was matched at a ratio of 1:5 with the fully vaccinated (i.e., 2 doses) and booster vaccinated groups (i.e., 3 doses) according to sex, age strata, calendar date, and contact settings. Multivariate conditional logistic regression models were adopted to estimate the marginal effectiveness of the booster dose against Omicron BA.5 infection after adjusting for confounding variables. Subgroup analyses were performed to assess vaccine effectiveness (VE) in different strata of sex, age, the time lag from the last vaccine dose to exposure, and the vaccination status of the source case. Kaplan–Meier curves were employed to visualize the follow-up process and testing outcomes among different subgroups of the matched cohort. Findings Before matching, 37,099 adult close contacts were eligible for cohort enrolment. After matching, the 2-dose and 3-dose groups included 3317 and 16,051 contacts, and the proportions with Omicron infections were 1.03% and 0.62% among contacts in the 2-dose and 3-dose groups, respectively. We estimated that the adjusted effectiveness of the inactivated booster vaccine versus 2 doses against Omicron infection was 35.5% (95% CI 2.0, 57.5). The booster dose provided a higher level of protection, with an effectiveness of 60.2% (95% CI 22.8, 79.5) for 15–180 days after vaccination, but this VE decreased to 35.0% (95% CI 2.8, 56.5) after 180 days. Evidence for the protection of the booster dose was detected among young adults aged 18–39 years, but was not detected for those aged 40 years or older. Interpretation The receipt of the inactivated vaccine booster dose was associated with a significantly lower Omicron infection risk, and our findings confirmed the vaccine effectiveness (VE) of booster doses against Omicron BA.5 variants. Given the rapid evolution of SARS-CoV-2, we highlight the importance of continuously monitoring the protective performance of vaccines against the genetic variants of SARS-CoV-2, regardless of existing vaccine coverage.

Abstract Introduction With COVID-19 vaccination rolled out globally, increasing numbers of studies have shown that booster vaccines can enhance an individual’s protection against the infection, hospitalization, and death caused by SARS-CoV-2. This study evaluated the effectiveness of COVID-19 vaccine BBIBP-CorV booster against being infected (susceptibility), infecting others (infectiousness), and spreading the disease from one to another (transmission). Methods This retrospective cohort study investigated the close contacts of all officially ascertained COVID-19 confirmed cases in Urumqi, China between August 1 and September 7, 2022. Eligible records were divided into four subcohorts based on the vaccination status of both the close contact and their source case: group 2-2, 2-dose contacts seeded by 2-dose source case (as the reference level); group 2-3, 3-dose contacts seeded by 2-dose source case; group 3-2, 2-dose contacts seeded by 3-dose source case; and group 3-3, 3-dose contacts seeded by 3-dose source case. In the four subcohorts, multivariate logistic regression models were used to examine the vaccine effectiveness (VE) for the BBIBP-CorV booster dose. We adjusted for potential confounding variables, including the sex and age of source cases and close contacts, the calendar week of contact history and contact settings. We evaluated the statistical uncertainty using a 95% confidence interval (CI). In addition, we conducted subgroup analyses to evaluate VE by sex. Results The sample sizes of groups 2-2, 2-3, 3-2, and 3-3 were 1184, 3773, 4723, and 27,136 individuals, respectively. Overall VE against susceptibility (group 2-3 vs 2-2) was 42.1% (95% CI 10.6, 62.5), VE against infectiousness (group 3-2 vs 2-2) was 62.0% (95% CI 37.2, 77.0), and VE against transmission (group 3-3 vs 2-2) was 83.7% (95% CI 75.1, 89.4). In the sex-stratified subgroups, male close contacts showed similar VE compared to the overall. However, among female close contacts, while the booster dose improved VE against infectiousness and VE against susceptibility, the VEs were not significantly different from zero. Conclusion BBIBP-CorV vaccine booster was associated with mild to moderate levels of protection against Omicron susceptibility, infectiousness, and transmission. Real-world assessment of protective performance of COVID-19 vaccines against the risk of Omicron strains is continuously needed, and may provide information that helps vaccination strategy.

The potential for dengue fever epidemic due to climate change remains uncertain in tropical areas. This study aims to assess the impact of climate change on dengue fever transmission in four South and Southeast Asian settings. We collected weekly data of dengue fever incidence, daily mean temperature and rainfall from 2012 to 2020 in Singapore, Colombo, Selangor, and Chiang Mai. Projections for temperature and rainfall were drawn for three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) scenarios. Using a disease transmission model, we projected the dengue fever epidemics until 2090s and determined the changes in annual peak incidence, peak time, epidemic size, and outbreak duration. A total of 684,639 dengue fever cases were reported in the four locations between 2012 and 2020. The projected change in dengue fever transmission would be most significant under the SSP585 scenario. In comparison to the 2030s, the peak incidence would rise by 1.29 times in Singapore, 2.25 times in Colombo, 1.36 times in Selangor, and >10 times in Chiang Mai in the 2090s under SSP585. Additionally, the peak time was projected to be earlier in Singapore, Colombo, and Selangor, but be later in Chiang Mai under the SSP585 scenario. Even in a milder emission scenario of SSP126, the epidemic size was projected to increase by 5.94%, 10.81%, 12.95%, and 69.60% from the 2030s–2090s in Singapore, Colombo, Selangor, and Chiang Mai, respectively. The outbreak durations in the four settings were projected to be prolonged over this century under SSP126 and SSP245, while a slight decrease is expected in 2090s under SSP585. The results indicate that climate change is expected to increase the risk of dengue fever transmission in tropical areas of South and Southeast Asia. Limiting greenhouse gas emissions could be crucial in reducing the transmission of dengue fever in the future.

Abstract Background The involvement of the central nervous system is a frequent yet underestimated complication of diabetes mellitus. Visual evoked potentials (VEP) are a simple, sensitive, and noninvasive method for detecting early alterations in central optic pathways. The objective of this paralleled randomized controlled trial was to evaluate the impact of ozone therapy on visual pathways in diabetic patients. Methods Sixty patients with type 2 diabetes visiting clinics of Baqiyatallah university in Tehran (Iran) hospital were randomly assigned to two experimental groups: Group 1 (N = 30) undergoing a cycle of 20 sessions of systemic oxygen-ozone therapy in addition to standard therapy for metabolic control; Group 2 (N = 30)—serving as control—receiving only standard therapy against diabetes. The primary study endpoints were two VEP parameters; P100 wave latency and P100 amplitude at 3 months. Moreover, HbA1c levels were measured before the start of treatment and three months later as secondary study endpoint. Results All 60 patients completed the clinical trial. P100 latency significantly reduced at 3 months since baseline. No correlation was found between repeated measures of P100 wave latency and HbA1c (Pearson’s r = 0.169, p = 0.291). There was no significant difference between baseline values and repeated measures of P100 wave amplitude over time in either group. No adverse effects were recorded. Conclusions Ozone therapy improved the conduction of impulses in optic pathways of diabetic patients. The improved glycemic control following ozone therpay may not fully explain the reduction of P100 wave latency though; other mechanistic effects of ozone may be involved.

Abstract Background Various nonpharmaceutical interventions (NPIs) against COVID-19 continue to have an impact on socioeconomic and population behaviour patterns. However, the effect of NPIs on notifiable infectious diseases remains inconclusive due to the variability of the disease spectrum, high-incidence endemic diseases and environmental factors across different geographical regions. Thus, it is of public health interest to explore the influence of NPIs on notifiable infectious diseases in Yinchuan, Northwest China. Methods Based on data on notifiable infectious diseases (NIDs), air pollutants, meteorological data, and the number of health institutional personnel in Yinchuan, we first fitted dynamic regression time series models to the incidence of NIDs from 2013 to 2019 and then estimated the incidence for 2020. Then, we compared the projected time series data with the observed incidence of NIDs in 2020. We calculated the relative reduction in NIDs at different emergency response levels in 2020 to identify the impacts of NIPs on NIDs in Yinchuan. Results A total of 15,711 cases of NIDs were reported in Yinchuan in 2020, which was 42.59% lower than the average annual number of cases from 2013 to 2019. Natural focal diseases and vector-borne infectious diseases showed an increasing trend, as the observed incidence in 2020 was 46.86% higher than the estimated cases. The observed number of cases changed in respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases were 65.27%, 58.45% and 35.01% higher than the expected number, respectively. The NIDs with the highest reductions in each subgroup were hand, foot, and mouth disease (5854 cases), infectious diarrhoea (2157 cases) and scarlet fever (832 cases), respectively. In addition, it was also found that the expected relative reduction in NIDs in 2020 showed a decline across different emergency response levels, as the relative reduction dropped from 65.65% (95% CI: -65.86%, 80.84%) during the level 1 response to 52.72% (95% CI: 20.84%, 66.30%) during the level 3 response. Conclusions The widespread implementation of NPIs in 2020 may have had significant inhibitory effects on the incidence of respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases. The relative reduction in NIDs during different emergency response levels in 2020 showed a declining trend as the response level changed from level 1 to level 3. These results can serve as essential guidance for policy-makers and stakeholders to take specific actions to control infectious diseases and protect vulnerable populations in the future.

Objectives: As the genetic variants of SARS-CoV-2 continuously pose threats to global health, evaluating superspreading potentials of emerging genetic variants is of importance for region-wide control of COVID-19 outbreaks. Methods: By using detailed epidemiological contact tracing data of test-positive COVID-19 cases collected between July and August 2021 in Nanjing and Yangzhou, China, we assessed the superspreading potential of outbreaks seeded by SARS-CoV-2 Delta variants. The transmission chains and case-clusters were constructed according to the individual-based surveillance data. We modelled the disease transmission as a classic branching process with transmission heterogeneity governed by negative binomial models. Subgroup analysis was conducted by different contact settings and age groups. Results: We reported a considerable heterogeneity in the contact patterns and transmissibility of Delta variants in eastern China. We estimated an expected 14% (95% CI: 11–16%) of the most infectious cases generated 80% of the total transmission. Conclusions: Delta variants demonstrated a significant potential of superspreading under strict control measures and active COVID-19 detecting efforts. Enhancing the surveillance on disease transmissibility especially in high-risk settings, along with rapid contact tracing and case isolations would be one of the key factors to mitigate the epidemic caused by the emerging genetic variants of SARS-CoV-2.

Nano-based drug delivery systems are increasingly used for diagnosis, prevention and treatment of several diseases, thanks to several beneficial properties, including the ability to target specific cells or organs, allowing to reduce treatment costs and side effects frequently associated with chemotherapeutic medications, thereby improving treatment compliance of patients. In the field of communicable diseases, especially those caused by intracellular bacteria, the delivery of antibiotics targeting specific cells is of critical importance to maximize their treatment efficacy. Brucella melitensis, an intracellular obligate bacterium surviving and replicating inside macrophages is hard to be eradicated, mainly because of the low ability of antibiotics to enter these phagocityc cells . Although different antibiotics regimens including gentamicin, doxycycline and rifampicin are in fact used against the Brucellosis, no efficient treatment has been attained yet, due to the intracellular life of the respective pathogen. Nano-medicines responding to environmental stimuli allow to maximize drug delivery targeting macropages, thereby boosting treatment efficacy. Several drug delivery nano-technologies, including solid lipid nanoparticles, liposomes, chitosan, niosomes, and their combinations with chitosan sodium alginate can be employed in combination of antibiotics to successfully eradicate Brucellosis infection from patients.

Purpose Schizophrenia (SCH) is a severe psychiatric disorder associated with brain connectivity abnormalities, and early diagnosis can significantly reduce the burden on the families of the patients. Though several classification methods have been created to identify SCH, a reliable method is yet to be found. In this study, we explore the performance of multidimensional spatial feature fusion in the recognition of SCH. Materials and methods Using an MRI connectomes dataset, we extract the spatial pattern network (SPN) and diffusion map embedding (DME) features from functional connectivity (FC) and structural connectivity (SC) networks of both schizophrenic patients and healthy subjects, and we use both single mode features and fused features to classify the two groups. Results Compared to the single mode features, the fused features showed superior performance in classification. By fusing the SPN and DME features of the structural network, we obtained the highest accuracy of 87.50%. Conclusions Multidimensional spatial feature fusion is promising as a reliable method for the recognition of SCH.

A polarization-insensitive thermo-optic switch is proposed and demonstrated on the silicon-on-insulator platform with a 220 nm silicon core layer. The present device is based on the Mach–Zehnder interferometer structure, consisting of polarization-insensitive power splitters and polarization-insensitive phase shifters (PIPSs). The polarization-insensitive power splitter has been realized by employing an adiabatic directional coupler, which utilizes the fast adiabatic mode evolution by introducing cubic Bézier curves on outer contours, providing broadband 3-dB power splitting for TE and TM polarization modes with only 70 µm coupling length. For the novel PIPSs, the ridge waveguide with large aspect ratio, based on the mode hybridness property, could obtain the same power consumption (Pπ) for an optical switch working at TE and TM polarizations. Experimental results indicate that the measured insertion losses are less than 2 dB and the extinction ratios are larger than 15 dB over a 40 nm wavelength band (covering the C-band).

Background: Although the COVID-19 pandemic has persisted for more than two years with the evident excess mortality from diabetes, few studies have investigated its temporal patterns. This study aims to estimate the excess deaths from diabetes in the United States (US) during the COVID-19 pandemic and evaluate the excess deaths by spatiotemporal pattern, age groups, sex, and race/ethnicity. Methods: Diabetes as one of multiple causes of death or an underlying cause of death were both considered into analyses. The Poisson log-linear regression model was used to estimate weekly expected counts of deaths during the pandemic with adjustments for long-term trend and seasonality. Excess deaths were measured by the difference between observed and expected death counts, including weekly average excess deaths, excess death rate, and excess risk. We calculated the excess estimates by pandemic wave, US state, and demographic characteristic. Results: From March 2020 to March 2022, deaths that diabetes as one of multiple causes of death and an underlying cause of death were about 47.6 % and 18.4 % higher than the expected. The excess deaths of diabetes had evident temporal patterns with two large percentage increases observed during March 2020, to June 2020, and June 2021 to November 2021. The regional heterogeneity and underlying age and racial/ethnic disparities of the excess deaths were also clearly observed. Conclusions: This study highlighted the increased risks of diabetes mortality, heterogeneous spatiotemporal patterns, and associated demographic disparities during the pandemic. Practical actions are warranted to monitor disease progression, and lessen health disparities in patients with diabetes during the COVID-19 pandemic.

Breast cancer, an unceasingly occurring neoplasm, is one of the major determinants of mortality in women. Several ineffective attempts have been pursued using with conventional therapies against breast cancer. Resistance to existing therapies and their respective debilitating adverse effects have led research toward a new era of cancer treatment using viruses. Virotherapy constitutes a developing treatment modality with multiple mechanisms of therapeutic activity in which the viruses can be directly oncolyticand can express transgenes or induce host immune response against tumor cells. Several different DNA- and RNA-containing viruses have been considered for virotherapy of breast cancer including adenovirus, herpes virus, vaccinia, reovirus, Newcastle Disease virus, measles virus and vesicular stomatitis virus. This review aims to summarize the viro-therapeutical agents against breast malignancies. Key Scientific Concepts of Review: In this review paper, we proposed a new strategy to virus's combinatorial treatments using several kinds of transgenes and drugs. These recombinant viruses have provided evidence of treatment efficacy against human breast cancer.

Abstract Background The high immune evasion ability of SARS-COV-2 Omicron variant surprised the world and appears to be far stronger than any previous variant. Previous to Omicron it has been difficult to assess and compare immune evasion ability of different variants, including the Beta and Delta variants, because of the relatively small numbers of reinfections and because of the problems in correctly identifying reinfections in the population. This has led to different claims appearing in the literature. Thus we find claims of both high and low immune evasion for the Beta variant. Some findings have suggested that the Beta variant has a higher immune evasion ability than the Delta variant in South Africa, and others that it has a lower ability. Method In this brief report, we re-analyse a unique dataset of variant-specific reinfection data and a simple model to correct for the infection attack rates of different variants. Result We find that a model with the Delta variant having an equal or higher immune evasion ability than Beta variant is compatible with the data. Conclusion We conclude that the immune evasion ability of Beta variant is not stronger than Delta variant, and indeed, the immune evasion abilities of both variants are weak in South Africa.

Virus evolution is a common process of pathogen adaption to host population and environment. Frequently, a small but important fraction of virus mutations are reported to contribute to higher risks of host infection, which is one of the major determinants of infectious diseases outbreaks at population scale. The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly. Based on classic epidemiology theories of disease transmission, we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population. The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness. The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England. The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population.

Patient-reported outcome (PRO) refers to any information about the patient's own health condition, functional status, and feelings about treatment that directly comes from the patient without interpretation by physicians, nurses or anyone else. Measuring outcomes from the patient perspective is conducive to improving the quality of medical services and treatment satisfaction. We summarized recent applications of PRO in many fields, such as drug review and approval, drug and medical equipment supervision, quality evaluation of medical services, health technology assessment, and comprehensive clinical evaluation of drugs, and reviewed the latest advances in research on PRO, such as the development of PRO instruments, the construction of a health state utility value set, the health measurement in children and adolescents, and the quality evaluation of PRO instruments. Then we put forward recommendations to further promote the studies and use of PRO in China: strengthening the development of PRO instruments applicable to China, valuing the use of PRO in clinical practice, enhancing the interdisciplinary research on PRO, broadening the profile of research on PRO, and intensifying relevant research.

Birth weight is an indicator linking intrauterine environmental exposures to later-life diseases, and intrauterine metal exposure may affect birth weight in a sex-specific manner. We investigated sex-specific associations between prenatal exposure to metal mixtures and birth weight in a Chinese birth cohort. The birth weight of 1296 boys and 1098 girls were recorded, and 10 metals in maternal urine samples collected during pregnancy were measured using inductively coupled plasma mass spectrometry. Bayesian Kernel Machine Regression was used to estimate the association of individual metals or metal mixtures and birth weight for gestational age (BW for GA). The model showed a sex-specific relationship between prenatal exposure to metal mixtures and BW for GA with a significant negative association in girls and a non-significant positive association in boys. Cadmium (Cd) and nickel (Ni) were positively and negatively associated with BW for GA in girls, respectively. Moreover, increasing thallium (Tl) concentration lowered the positive association between Cd and BW for GA and enhanced the negative association between Ni and BW for GA in girls. When exposure to other metals increased, the positive association with Cd diminished, whereas the negative association with Ni or Tl increased. Our findings provide evidence supporting the complex effects of intrauterine exposure to metal mixtures on the birth weight of girls and further highlight the sex heterogeneity in fetal development influenced by intrauterine environmental factors.

Objectives: To evaluate excess deaths of gastrointestinal, liver, and pancreatic diseases in the United States during the COVID-19 pandemic.Methods: We retrieved weekly death counts from National Vital Statistics System and fitted them with a quasi-Poisson regression model. Cause-specific excess deaths were calculated by the difference between observed and expected deaths with adjustment for temporal trend and seasonality. Demographic disparities and temporal-spatial patterns were evaluated for different diseases.Results: From March 2020 to September 2022, the increased mortality (measured by excess risks) for Clostridium difficile colitis, gastrointestinal hemorrhage, and acute pancreatitis were 35.9%; 24.8%; and 20.6% higher than the expected. For alcoholic liver disease, fibrosis/cirrhosis, and hepatic failure, the excess risks were 1.4–2.8 times higher among younger inhabitants than older inhabitants. The excess deaths of selected diseases were persistently observed across multiple epidemic waves with fluctuating trends for gastrointestinal hemorrhage and fibrosis/cirrhosis and an increasing trend for C. difficile colitis.Conclusion: The persistently observed excess deaths of digestive diseases highlights the importance for healthcare authorities to develop sustainable strategies in response to the long-term circulating of SARS-CoV-2 in the community.

Abstract Background Superspreading events (SSEs) played a critical role in fueling the COVID-19 outbreaks. Although it is well-known that COVID-19 epidemics exhibited substantial superspreading potential, little is known about the risk of observing SSEs in different contact settings. In this study, we aimed to assess the potential of superspreading in different contact settings in Japan. Method Transmission cluster data from Japan was collected between January and July 2020. Infector-infectee transmission pairs were constructed based on the contact tracing history. We fitted the data to negative binomial models to estimate the effective reproduction number (R) and dispersion parameter (k). Other epidemiological issues relating to the superspreading potential were also calculated. Results The overall estimated R and k are 0.561 (95% CrI: 0.496, 0.640) and 0.221 (95% CrI: 0.186, 0.262), respectively. The transmission in community, healthcare facilities and school manifest relatively higher superspreading potentials, compared to other contact settings. We inferred that 13.14% (95% CrI: 11.55%, 14.87%) of the most infectious cases generated 80% of the total transmission events. The probabilities of observing superspreading events for entire population and community, household, health care facilities, school, workplace contact settings are 1.75% (95% CrI: 1.57%, 1.99%), 0.49% (95% CrI: 0.22%, 1.18%), 0.07% (95% CrI: 0.06%, 0.08%), 0.67% (95% CrI: 0.31%, 1.21%), 0.33% (95% CrI: 0.13%, 0.94%), 0.32% (95% CrI: 0.21%, 0.60%), respectively. Conclusion The different potentials of superspreading in contact settings highlighted the need to continuously monitoring the transmissibility accompanied with the dispersion parameter, to timely identify high risk settings favoring the occurrence of SSEs.

To model estimated deaths averted by COVID-19 vaccines, we used state-of-the-art mathematical modeling, likelihood-based inference, and reported COVID-19 death and vaccination data. We estimated that >1.5 million deaths were averted in 12 countries. Our model can help assess effectiveness of the vaccination program, which is crucial for curbing the COVID-19 pandemic.

Objectives Direct comparisons between COVID-19 and influenza A in the critical care setting are limited. The objective of this study was to compare their outcomes and identify risk factors for hospital mortality.Design and setting This was a territory-wide, retrospective study on all adult (≥18 years old) patients admitted to public hospital intensive care units in Hong Kong. We compared COVID-19 patients admitted between 27 January 2020 and 26 January 2021 with a propensity-matched historical cohort of influenza A patients admitted between 27 January 2015 and 26 January 2020. We reported outcomes of hospital mortality and time to death or discharge. Multivariate analysis using Poisson regression and relative risk (RR) was used to identify risk factors for hospital mortality.Results After propensity matching, 373 COVID-19 and 373 influenza A patients were evenly matched for baseline characteristics. COVID-19 patients had higher unadjusted hospital mortality than influenza A patients (17.5% vs 7.5%, p

The COVID-19 pandemic caused multiple waves of mortality in South Africa, where three genetic variants of SARS-COV-2 and their ancestral strain dominated consecutively. State-of-the-art mathematical modeling approach was used to estimate the time-varying transmissibility of SARS-COV-2 and the relative transmissibility of Beta, Delta, and Omicron variants. The transmissibility of the three variants were about 73%, 87%, and 276% higher than their preceding variants. To the best of our knowledge, our model is the first simple model that can simulate multiple mortality waves and three variants' replacements in South Africa. The transmissibility of the Omicron variant is substantially higher than that of previous variants.

The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess (DP) ship has caused over 634 cases as of February 20, 2020. We model the transmission process on DP ship as a stochastic branching process, and estimate the reproduction number at the innitial phase of 2.9 (95%CrI: 1.7–7.7). The epidemic doubling time is 3.4 days, and thus timely actions on COVID-19 control were crucial. We estimate the COVID-19 transmissibility reduced 34% after the quarantine program on the DP ship which was implemented on February 5. According to the model simulation, relocating the population at risk may sustainably decrease the epidemic size, postpone the timing of epidemic peak, and thus relieve the tensive demands in the healthcare. The lesson learnt on the ship should be considered in other similar settings.

Since March 11th, 2020, COVID-19 has been a global pandemic for more than one years due to a long and infectious incubation period. This paper establishes a heterogeneous epidemic model that divides the incubation period into infectious and non-infectious and employs the Bayesian framework to model the ‘Diamond Princess’ enclosed space incident. The heterogeneity includes two different identities, two transmission methods, two different-size rooms, and six transmission stages. This model is also applicable to similar mixed structures, including closed schools, hospitals, and communities. As the COVID-19 pandemic continues, our mathematical modeling can provide management insights to the governments and policymakers on how the COVID-19 disease has spread and what prevention strategies still need to be taken.

Objectives: Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which makes the serological data difficult to interpret. In this work, we aim to solve this issue. Methods: We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-19 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results: Our model simulated seroprevalence matchs the reported seroprevalence in most of the 12 Indian cities. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions: Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.

BackgroundWhile many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19. ObjectiveWe employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events. MethodsWe retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc. ResultsA total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%). ConclusionsWe demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events.

Timely detection of an evolving event of an infectious disease with superspreading potential is imperative for territory-wide disease control as well as preventing future outbreaks. While the reproduction number (R) is a commonly-adopted metric for disease transmissibility, the transmission heterogeneity quantified by dispersion parameter k, a metric for superspreading potential is seldom tracked. In this study, we developed an estimation framework to track the time-varying risk of superspreading events (SSEs) and demonstrated the method using the three epidemic waves of COVID-19 in Hong Kong. Epidemiological contact tracing data of the confirmed COVID-19 cases from 23 January 2020 to 30 September 2021 were obtained. By applying branching process models, we jointly estimated the time-varying R and k. Individual-based outbreak simulations were conducted to compare the time-varying assessment of the superspreading potential with the typical non-time-varying estimate of k over a period of time. We found that the COVID-19 transmission in Hong Kong exhibited substantial superspreading during the initial phase of the epidemics, with only 1 % (95 % Credible interval [CrI]: 0.6–2 %), 5 % (95 % CrI: 3–7 %) and 10 % (95 % CrI: 8–14 %) of the most infectious cases generated 80 % of all transmission for the first, second and third epidemic waves, respectively. After implementing local public health interventions, R estimates dropped gradually and k estimates increased thereby reducing the risk of SSEs to approaching zero. Outbreak simulations indicated that the non-time-varying estimate of k may overlook the possibility of large outbreaks. Hence, an estimation of the time-varying k as a compliment of R as a monitoring of both disease transmissibility and superspreading potential, particularly when public health interventions were relaxed is crucial for minimizing the risk of future outbreaks.

Background: The coronavirus disease 2019 (COVID-19) pandemic has become a major public health threat. This study aims to evaluate the effect of virus mutation activities and policy interventions on COVID-19 transmissibility in Hong Kong. Methods: In this study, we integrated the genetic activities of multiple proteins, and quantified the effect of government interventions and mutation activities against the time-varying effective reproduction number Rt. Findings: We found a significantly positive relationship between Rt and mutation activities and a significantly negative relationship between Rt and government interventions. The results showed that the mutations that contributed most to the increase of Rt were from the spike, nucleocapsid and ORF1b genes. Policy of prohibition on group gathering was estimated to have the largest impact on mitigating virus transmissibility. The model explained 63.2% of the Rt variability with the R2. Conclusion: Our study provided a convenient framework to estimate the effect of genetic contribution and government interventions on pathogen transmissibility. We showed that the S, N and ORF1b protein had significant contribution to the increase of transmissibility of SARS-CoV-2 in Hong Kong, while restrictions of public gathering and suspension of face-to-face class are the most effective government interventions strategies.

In this work, we report a large-scale synchronized replacement pattern of the Alpha (B.1.1.7) variant by the Delta (B.1.617.2) variant of SARS-COV-2. We argue that this phenomenon is associated with the invasion timing and the transmissibility advantage of the Delta (B.1.617.2) variant. Alpha (B.1.1.7) variant skipped some countries/regions, e.g. India and neighboring countries/regions, which could have led to a mild first wave before the invasion of the Delta (B.1.617.2) variant, in term of reported COVID-deaths per capita.

We propose a large scanning range silicon optical phased array (OPA), which is polarization multiplexed and bi-directional with only one waveguide grating antenna array. Mach-Zehnder interferometers (MZIs) and polarization splitter-rotator (PSR) are utilized to select the polarization states and propagation directions of the input light to the waveguide grating emitter array. By optimizing the parameters of the waveguide grating, the longitudinal far-field steering range is increased by four times compared to that of the conventional ones among the same wavelength range. The simulation results show that the 54.5° longitudinal scanning range could be realized with the tuning wavelength ranging from 1500 nm to 1600 nm. The high wavelength tuning efficiency is up to 0.545 °/nm. The beam steering range in the lateral direction is 77.8° via phase tuning.

By February 2021, the overall impact of the COVID-19 pandemic in India had been relatively mild in terms of total reported cases and deaths. Surprisingly, the second wave in early April becomes devastating and attracts worldwide attention. Multiple factors (e.g., Delta variants with increased transmissibility) could have driven the rapid growth of the epidemic in India and led to a large number of deaths within a short period. We aim to reconstruct the transmission rate, estimate the infection fatality rate and forecast the epidemic size. We download the reported COVID-19 mortality data in India and formulate a simple mathematical model with a flexible transmission rate. We use iterated filtering to fit our model to deaths data. We forecast the infection attack rate in a month ahead. Our model simulation matched the reported deaths well and is reasonably close to the results of the serological study. We forecast that the infection attack rate (IAR) could have reached 43% by July 24, 2021, under the current trend. Our estimated infection fatality rate is about 0.07%. Under the current trend, the IAR will likely reach a level of 43% by July 24, 2021. Our estimated infection fatality rate appears unusually low, which could be due to a low case to infection ratio reported in previous study. Our approach is readily applicable in other countries and with other types of data (e.g., excess deaths).

Introduction: To determine the stage-specific incidence trend of hepatocellular carcinoma (HCC) among US adults. Methods: The age-adjusted incidence rate was extracted from Surveillance, Epidemiology, and End Results database for localized, regional and distant HCC. Trend analyses were conducted in the overall population and stratified by demographic and sociodemographic variables. The annual percentage change (APC) in 2014-2019 was estimated to determine the stage-specific incidence trend. Results: Although the incidence in localized HCC significantly declined, the incidence for regional and distant HCC plateaued in 2014-2019 (APCs, 4.4% [95% CI, -0.2% to 9.3%] and -0.7% [95% CI, -1.8% to 0.5%], respectively) with age and race/ethnicity disparities. More pronounced increases for regional and distant HCC were observed among the elderly (APCs, 8.4% [95% CI, 4.8% to 12.2%] and 2.2% [95% CI, 1.7% to 2.7%] for regional and distant HCC, respectively), non-Hispanic White individuals (APCs, 4.0% [95% CI, 2.9% to 5.1%] and 1.5% [95% CI, 0.7% to 2.4%] for regional and distant HCC, respectively). Conclusions: Disparities in incidence trends may reflect the inequalities in access to primary health care. More efforts are still in great demand for the vulnerable population.

Based on exposure history and symptom onset of 22 Omicron BA.1 cases in South Korea from November to December 2021, we estimated mean incubation period of 3.5 days (95% CI: 2.5, 3.8), and then compared to that of 6.5 days (95% CI: 5.3, 7.7) for 64 cases during Delta variants' dominance in June 2021. For Omicron BA.1 variants, we found that 95% of symptomatic cases developed clinical conditions within 6.0 days (95% CI: 4.3, 6.6) after exposure. Thus, a shorter quarantine period may be considered based on symptoms, or similarly laboratory testing, when Omicron BA.1 variants are circulating.

BackgroundAs of August 25, 2021, Jiangsu province experienced the largest COVID-19 outbreak in eastern China that was seeded by SARS-CoV-2 Delta variants. As one of the key epidemiological parameters characterizing the transmission dynamics of COVID-19, the incubation period plays an essential role in informing public health measures for epidemic control. The incubation period of COVID-19 could vary by different age, sex, disease severity, and study settings. However, the impacts of these factors on the incubation period of Delta variants remains uninvestigated. ObjectiveThe objective of this study is to characterize the incubation period of the Delta variant using detailed contact tracing data. The effects of age, sex, and disease severity on the incubation period were investigated by multivariate regression analysis and subgroup analysis. MethodsWe extracted contact tracing data of 353 laboratory-confirmed cases of SARS-CoV-2 Delta variants’ infection in Jiangsu province, China, from July to August 2021. The distribution of incubation period of Delta variants was estimated by using likelihood-based approach with adjustment for interval-censored observations. The effects of age, sex, and disease severity on the incubation period were expiated by using multivariate logistic regression model with interval censoring. ResultsThe mean incubation period of the Delta variant was estimated at 6.64 days (95% credible interval: 6.27-7.00). We found that female cases and cases with severe symptoms had relatively longer mean incubation periods than male cases and those with nonsevere symptoms, respectively. One-day increase in the incubation period of Delta variants was associated with a weak decrease in the probability of having severe illness with an adjusted odds ratio of 0.88 (95% credible interval: 0.71-1.07). ConclusionsIn this study, the incubation period was found to vary across different levels of sex, age, and disease severity of COVID-19. These findings provide additional information on the incubation period of Delta variants and highlight the importance of continuing surveillance and monitoring of the epidemiological characteristics of emerging SARS-CoV-2 variants as they evolve.

BackgroundIncreasing evidence suggests that the number of examined lymph nodes (ELNs) is strongly linked to the survivorship of gastric cancer (GC). The goal of this study was to assess the prognostic implications of the ELNs number and to construct an ELNs-based risk signature and nomogram model to predict overall survival (OS) characteristics in GC patients.MethodsThis inception cohort study included 19,317 GC patients from the U.S. Surveillance, Epidemiology, and End Results (SEER) database, who were separated into a training group and an internal validation group. The nomogram was built with the training set, then internally verified with SEER data, and externally validated with two different data sets. Based on the RNA-seq data, ELNs-related DERNAs (DElncRNAs, DEmiRNAs, andDEmRNAs) and immune cells were identified. The LASSO–Cox regression analysis was utilized to construct ELNs-related DERNAs and immune cell prognostic signature in The Cancer Genome Atlas (TCGA) cohort. The OS of subgroups with high- and low-ELN signature was compared using the Kaplan–Meier (K-M) analysis. A nomogram was successfully constructed based on the ELNs signature and other clinical characteristics. The concordance index (C-index), calibration plot, receiver operating characteristic curve, and decision curve analysis (DCA) were all used to evaluate the nomogram model. The meta-analysis, the Gene Expression Profiling Interactive Analysis database, and reverse transcription–quantitative PCR (RT-qPCR) were utilized to validate the RNA expression or abundance of prognostic genes and immune cells between GC tissues and normal gastric tissues, respectively. Finally, we analyzed the correlations between immune checkpoints, chemotherapy drug sensitivity, and risk score.ResultsThe multivariate analysis revealed that the high ELNs improved OS compared with low ELNs (hazard ratio [HR] = 0.659, 95% confidence interval [CI]: 0.626–0.694, p < 0.0001). Using the training set, a nomogram incorporating ELNs was built and proven to have good calibration and discrimination (C-index [95% CI], 0.714 [0.710–0.718]), which was validated in the internal validation set (C-index [95% CI], 0.720 [0.714–0.726]), the TCGA set (C-index [95% CI], 0.693 [0.662–0.724]), and the Chinese set (C-index [95% CI], 0.750 [0.720–0.782]). An ELNs-related signature model based on ELNs group, regulatory T cells (Tregs), neutrophils, CDKN2B-AS1, H19, HOTTIP, LINC00643, MIR663AHG, TMEM236, ZNF705A, and hsa-miR-135a-5p was constructed by the LASSO–Cox regression analysis. The result showed that OS was remarkably lower in patients with high-ELNs signature compared with those with low-ELN signature (HR = 2.418, 95% CI: 1.804–3.241, p < 0.001). This signature performed well in predicting 1-, 3-, and 5-year survival (AUC [95% CI] = 0.688 [0.612–0.763], 0.744 [0.659–0.830], and 0.778 [0.647–0.909], respectively). The multivariate Cox analysis illustrated that the risk score was an independent predictor of survival for patients with GC. Moreover, the expression of prognostic genes (LINC00643, TMEM236, and hsa-miR-135a-5p) displayed differences between GC tissues and adjacent non-tumor tissues. The C-index of the nomogram that can be used to predict the OS of GC patients was 0.710 (95% CI: 0.663–0.753). Both the calibration plots and DCA showed that the nomogram has good predictive performance. Moreover, the signature was significantly correlated with the N stage and T stage. According to our analysis, GC patients in the low-ELN signature group may have a better immunotherapy response and OS outcome.ConclusionsWe explored the prognostic role of ELNs in GC and successfully constructed an ELNs signature linked to the GC prognosis in TCGA. The findings manifested that the signature is a powerful predictive indicator for patients with GC. The signature might contain potential biomarkers for treatment response prediction for GC patients. Additionally, we identified a novel and robust nomogram combining the characteristics of ELNs and clinical factors for predicting 1-, 3-, and 5-year OS in GC patients, which will facilitate personalized survival prediction and aid clinical decision-making in GC patients.

BackgroundConcerning the changes in the prevalence of neurodevelopmental disorders (NDDs), we estimate the prevalence of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), intellectual disorder (ID), and learning disability (LD) among US children and adolescents aged 3–17 years in 2019 and 2020.MethodsThe study includes 14,983 US children and adolescents aged 3–17 years in 2019 and 2020 from the National Health Interview Survey (NHIS). Parents were interviewed about whether their children ever and/or currently had NDDs diagnosed. Prevalence estimates of NDDs were calculated with a survey-based weighting scheme. Logistic regression models were used to estimate the associations between NDDs prevalence and subgroups.ResultsThe weighted prevalence of ADHD, ASD, ID, and LD was 8.5% (95% CI: 7.9–9.2%), 2.9% (95% CI: 2.6–3.4%), 1.4% (95% CI: 1.2–1.7%), and 6.4% (95% CI: 5.8–7.0%), respectively. A higher prevalence of ADHD, ASD, ID, and LD was observed in boys, those who ever had anxiety or depression symptoms, those with lower family income, those living in a rented house, ever been bullied, and ever lived with anyone mentally ill.ConclusionThe study found the prevalence of ADHD, ASD, ID, and LD was different by demographics, comorbidity/mental problems, household/parental characteristics, and stressful life events.

This study aims to estimate the number of COVID-19 cases under- ascertained (η), and the basic reproduction number (R0) during the early stage of epidemic in Kano, Nigeria. We adopt a simple exponential growth model to capture the patterns of COVID-19 early epidemic curve in Kano. The R0 is estimated at 2.7 (95%CI: 2.5, 3.0). We find that the number of COVID-19 cases under-ascertained likely occurred during the fourth week of April 2020, and should be considered for future epidemiological investigations and mitigation plan.

Abstract Background The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 genomes, e.g., the D614G substitution, is one of the major challenges of disease control. Characterizing the role of the mutation activities is of importance to understand how the evolution of pathogen shapes the epidemiological outcomes at population scale. Methods We developed a statistical framework to reconstruct variant-specific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihood-based approach, the model is exemplified with the COVID-19 surveillance data from January 1 to June 30, 2020 in California, USA. We explore the potential of this framework to generate early warning signals for detecting transmission advantage on a real-time basis. Results The modelling framework in this study links together the mutation activity at molecular scale and COVID-19 transmissibility at population scale. We find a significant transmission advantage of COVID-19 associated with the D614G substitution, which increases the infectivity by 54% (95%CI: 36, 72). For the early alarming potentials, the analytical framework is demonstrated to detect this transmission advantage, before the mutation reaches dominance, on a real-time basis. Conclusions We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework.

Abstract Recent studies on the pathophysiology of COVID-19 are indicating that the Angiotensin convertase enzyme 2 (ACE-2) and transmembrane serine protease 2 (TMPRSS2) can act as a major component in the fusion of SARS-Cov-2 with target cells. It has also been observed that the expression of ACE-2 and TMPRSS2 can be altered in malignancies. Shedding light on this matter could be crucial since the COVID-19 pandemic interfered with many gastrointestinal cancer screening programs. Herein we discuss the possibility of severe forms of COVID-19 in patients with gastrointestinal cancers due to the gastrointestinal entry route of SARS-CoV-2 into the human body. The disruption of cancer screening programs caused by the current COVID-19 pandemic could therefore have massive negative health impact on patients affected by gastrointestinal malignancies.

As the COVID-19 pandemic poses serious threats to global public health, Nigeria faces a potential public health crisis owing to COVID-19 and other infectious diseases, such as Lassa fever (LF) and malaria. In this study, we discuss the possible determinants behind the decreased number of LF cases in Nigeria, which was likely due to the synergistic impact of the COVID-19 pandemic. During the COVID-19 pandemic, the epidemic curve of LF seems to have deviated from the general seasonal scale seen in past years, which could be due to underreporting of cases. In addition, partial compliance with nonpharmaceutical interventions, limited resources, or human behavior could be contributing factors. Thus, we suggest that better differentiation in terms of human and resource allocation between COVID-19 and LF could help curtail the transmission effectively.