Impact of the COVID-19 pandemic on the hospital attendance of patients with primary cervical cancer in Heilongjiang, China.

Information regarding the impact of the coronavirus disease 2019 (COVID-19) pandemic on cervical cancer in mainland China is lacking. We explored its impact on the hospital attendance of patients with primary cervical cancer. We included 1918 patients with primary cervical cancer who initially attended Harbin Medical University Cancer Hospital between January 23, 2019, and January 23, 2021. Attendance decreased by 31%, from 1135 in 2019 to 783 in 2020, mainly from January to June (𝜒2 = 73.362, P < .001). The percentage of patients detected by screening decreased from 12.1% in January-June 2019 to 5.8% in January-June 2020 (𝜒2 = 7.187, P = .007). Patients with stage I accounted for 28.4% in 2020 significantly lower than 36.6% in 2019 (𝜒2 = 14.085, P < .001), and patients with stage III accounted for 27.1% in 2020 significantly higher than 20.5% in 2019 (𝜒2 = 11.145, P < .001). Waiting time for treatment was extended from 8 days (median) in January-June and July-December 2019 to 16 days in January-June (𝜒2 = 74.674, P < .001) and 12 days in July-December 2020 (𝜒2 = 37.916, P < .001). Of the 179 patients who delayed treatment, 164 (91.6%) were for the reasons of the healthcare providers. Compared to 2019, the number of patients in Harbin or non-Harbin in Heilongjiang Province and outside the province decreased, and cross-regional medical treatment has been hindered. The COVID-19 pandemic has negatively impacted cervical cancer patient attendance at the initial phase. These results are solid evidence that a strategy and mechanism for the effective attendance of cervical cancer patients in response to public health emergencies is urgently needed.

The effectiveness of the fully online flipped classroom for nursing undergraduates during the COVID-19: Historical control study.

AIM: Evaluate the fully online flipped classroom's effects during the pandemic. DESIGN: A comparative descriptive study with historical control design. METHODS: In an internal medicine nursing course, the fully online flipped classroom instruction was used with 53 undergraduates in 2020. Their final examinations were compared with the 50 students taught with traditional offline methods in 2019. Online interactions and students' experiences were described. Pass rates in both classes were over 90% (Χ2 = 0.276, p = 0.60), but the median score in 2019 was higher than in 2020 (Z = -2.491, p = 0.01). There were 996 online interactions and 734 valid interactions in total. All 49 students believed the online flipped classroom schedule was reasonable and all but three said it was helpful. However, 19 students (39%) felt traditional teaching is more effective. CONCLUSIONS: The fully online flipped classroom method was fairly effective during the pandemic. This model also did increase class participation and sufficient faculty-student interactions in remote education. However, fewer students earned outstanding scores, with possible reasons including the online flipped classroom, lack of clinical practice, stress from COVID-19 and the shortened exam time. Overall, the method is worth recommending under public health emergencies like COVID-19, and future research exploring potential concerns about scores is necessary.

Global border restrictions in 2020-2021: Adherence and the effectiveness in long-term COVID-19 epidemic control.

BACKGROUND: Restrictions on international travel were widely applied to contain cross-border COVID-19 diffusion, while such applications varied globally, and little was known about their impacts on the long-term epidemic progression. METHODS: We explored the global diversity in maintaining border policies classified to four levels (screening, quarantine, ban on regions and total border closure) using data of 185 countries and regions between 01 January 2020 to 31 December 2021. By using Ordinary least squares (OLS) regression and quantile regression (QR) models, we examined the relationship between total COVID-19 incidence and the cumulative duration of each policy level in 2020-2021, and the heterogeneity of such association across different transmission severity countries. RESULTS: Firstly, "ban on regions" was the most durable policy applied in high-income countries, while in low-income countries, less stringent measures of screening and quarantine arrivals were applied the longest. Secondly, the cumulatively longer maintenance of the border quarantine was significantly associated with lower infections (log) in COVID-19 high-prevalent countries (75th QR, coefficient estimates [ß] = -0.0038, 95% confidence interval: -0.0066 to -0.0010). By contrast, in medium and high transmission severity countries, those with longer duration of imposing bans on regions showed no suppressing effects but significantly higher COVID-19 incidence (OLS regression, ß = 0.0028, 95% CI: 0.0009-0.0047; 75th QR, ß = 0.0039, 95% CI: 0.0014-0.0063). No other significant results were found. CONCLUSION: From the long-term perspective, inbound quarantine was effective in mitigating severe epidemics. However, in countries with medium or high COVID-19 prevalence, our findings of ban on regions highlighted its ineffectiveness in the long-term epidemic progression.

Comparative analysis of SARS-CoV-2 Omicron BA.2.12.1 and BA.5.2 variants.

The initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron subvariants, BA.1 and BA.2, are being progressively displaced by BA.5 in many countries. To provide insight on the replacement of BA.2 by BA.5 as the dominant SARS-CoV-2 variant, we performed a comparative analysis of Omicron BA.2.12.1 and BA.5.2 variants in cell culture and hamster models. We found that BA.5.2 exhibited enhanced replicative kinetics over BA.2.12.1 in vitro and in vivo, which is evidenced by the dominant BA.5.2 viral genome detected at different time points, regardless of immune selection pressure with vaccine-induced serum antibodies. Utilizing reverse genetics, we constructed a mutant SARS-CoV-2 carrying spike F486V substitution, which is an uncharacterized mutation that concurrently discriminates Omicron BA.5.2 from BA.2.12.1 variant. We noticed that the 486th residue does not confer viral replication advantage to the virus. We also found that 486V displayed generally reduced immune evasion capacity when compared with its predecessor, 486F. However, the surge of fitness in BA.5.2 over BA.2.12.1 was not due to stand-alone F486V substitution but as a result of the combination of multiple mutations. Our study upholds the urgency for continuous monitoring of SARS-CoV-2 Omicron variants with enhanced replication fitness.

Olumiant® (Baricitinib)-A Newly Approved Janus Kinase Inhibitor for the Treatment of Alopecia Areata

Olumiant® (Baricitinib) is a newly approved treatment for alopecia areata. Baricitinib is a selective and reversible inhibitor of Janus kinase that has shown promising results in two randomized, placebo-controlled phase-3 trials. A significantly higher number of patients achieved at least 80% scalp coverage following 36 weeks of treatment, compared to the placebo group. Adverse effects reported include acne and urinary tract infections, in addition to the warnings and precautions as highlighted in the product monograph. The current recommended regimen is 2 mg or 4 mg taken once daily, depending on the extent of hair loss. Other approved indications for baricitinib treatment include management of rheumatoid arthritis and the COVID-19 infections. (SKINmed. 2022;20:452-455).

Same-Day Delivery with Fair Customer Service

The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic. The fast growth is not without its challenge. In 2016, due to low concentrations of memberships and far distance from the depot, certain minority neighborhoods were excluded from receiving Amazon’s SDD service, raising concerns about fairness. In this paper, we study the problem of offering fair SDD service to customers. The service area is partitioned into different regions. Over the course of a day, customers request for SDD service, and the timing of requests and delivery locations are not known in advance. The dispatcher dynamically assigns vehicles to make deliveries to accepted customers before their delivery deadline. In addition to overall service rate (utility), we maximize the minimal regional service rate across all regions (fairness). We model the problem as a multi-objective Markov decision process and develop a deep Q-learning solution approach. We introduce a novel transformation of learning from rates to actual services, which creates a stable and efficient learning process. Computational results demonstrate the effectiveness of our approach in alleviating unfairness both spatially and temporally in different customer geographies. We show this effectiveness is valid with different depot locations, providing businesses with an opportunity to achieve better fairness from any location. We also show that the proposed approach performs efficiently when serving heterogeneously wealthy districts in the city.

Mobile Phone-Based Population Flow Data for the COVID-19 Outbreak in Mainland China

Background Human migration is one of the driving forces for amplifying localized infectious disease outbreaks into widespread epidemics. During the outbreak of COVID-19 in China, the travels of the population from Wuhan have furthered the spread of the virus as the period coincided with the world's largest population movement to celebrate the Chinese New Year. Methods We have collected and made public an anonymous and aggregated mobility dataset extracted from mobile phones at the national level, describing the outflows of population travel from Wuhan. We evaluated the correlation between population movements and the virus spread by the dates when the number of diagnosed cases was documented. Results From Jan 1 to Jan 22 of 2020, a total of 20.2 million movements of at-risk population occurred from Wuhan to other regions in China. A large proportion of these movements occurred within Hubei province (84.5%), and a substantial increase of travels was observed even before the beginning of the official Chinese Spring Festival Travel. The outbound flows from Wuhan before the lockdown were found strongly correlated with the number of diagnosed cases in the destination cities (log-transformed). Conclusions The regions with the highest volume of receiving at-risk populations were identified. The movements of the at-risk population were strongly associated with the virus spread. These results together with province-by-province reports have been provided to governmental authorities to aid policy decisions at both the state and provincial levels. We believe that the effort in making this data available is extremely important for COVID-19 modelling and prediction.

Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics.

Markov state models (MSMs) play a key role in studying protein conformational dynamics. A sliding count window with a fixed lag time is widely used to sample sub-trajectories for transition counting and MSM construction. However, sub-trajectories sampled with a fixed lag time may not perform well under different selections of lag time, which requires strong prior practice and leads to less robust estimation. To alleviate it, we propose a novel stochastic method from a Poisson process to generate perturbative lag time for sub-trajectory sampling and utilize it to construct a Markov chain. Comprehensive evaluations on the double-well system, WW domain, BPTI, and RBD-ACE2 complex of SARS-CoV-2 reveal that our algorithm significantly increases the robustness and power of a constructed MSM without disturbing the Markovian properties. Furthermore, the superiority of our algorithm is amplified for slow dynamic modes in complex biological processes.

Insomnia and sleep duration on COVID-19 susceptibility and hospitalization: A Mendelian randomization study.

Background: Sleep disturbance including insomnia and sleep duration is associated with an increased risk of infectious. With the ongoing coronavirus disease 2019 (COVID-19) pandemic, it is important to explore potential causal associations of sleep disturbance on COVID-19 susceptibility and hospitalization. Method: Insomnia and sleep duration were selected as exposure. Outcomes included susceptibility and hospitalization for COVID-19. Two sample mendelian randomization design was used to assess causality between sleep and COVID-19. Inverse variance weighted method was used as main analysis method to combine the ratio estimates for each instrumental variable to obtain the causal effect. Cochran's Q statistic was used to test for global heterogeneity. MR-Egger and weighting median estimator (WME) were used as sensitivity analysis to ensure the stability and reliability of the results. MR-Egger intercept term was used to test the mean pleiotropy. In addition, the direct effects of insomnia and sleep duration on COVID-19 susceptibility and hospitalization were estimated using multivariable mendelian randomization (MVMR). Results: Univariate MR provided no evidence of a causal associations of insomnia on COVID-19 susceptibility (OR = 1.10, 95% CI:0.95, 1.27; p = 0.21) and hospitalization (OR = 0.61, 95% CI:0.40, 0.92; p = 0.02); as does sleep duration (ORCOIVD - 19susceptibility = 0.93, 95% CI:0.86, 1.01; p = 0.07; ORCOIVD - 19 hospitalization = 1.21, 95% CI: 0.99, 1.47; p = 0.08). MVMR results showed that insomnia may be a risk factor for increased susceptibility to COVID-19 (OR = 1.65, 95% CI: 1.34, 2.05; p <0.001); and sleep duration was also associated with increased COVID-19 susceptibility (OR = 1.31, 95% CI: 1.18, 1.46; p < 0.001). Conclusion: Insomnia and extreme sleep duration may risk factors for increased COVID-19 susceptibility. Relieving insomnia and maintaining normal sleep duration may be powerful measures to reduce COVID-19 infections.

FUTUREBAPROF: DEMYSTIFYING ACADEMIC CAREERS IN BUSINESS ANALYTICS DEPARTMENTS

Inspired by NextProf, a program designed to diversify faculty positions in engineering started by the University of Michigan, Campbell came up with the idea for FutureBAProf, a workshop for PhD students and postdocs to demystify academic careers in business analytics. It was particularly aimed at those from underrepresented groups and designed to diversify application pools at all business analytics departments, not just University of Iowa. Originally planned for 2020, FutureBAProf was postponed due to COVID-19 until Aug 15-16, 2022, at the University of Iowa campus in Iowa City. More than 50 people from 33 different universities applied for the workshop and 22 were selected, including a mix of PhD students and postdocs from 21 different schools.

Neuropilin-1 Facilitates Pseudorabies Virus Replication and Viral Glycoprotein B Promotes Its Degradation in a Furin-Dependent Manner.

Pseudorabies virus (PRV), which is extremely infectious and can infect numerous mammals, has a risk of spillover into humans. Virus-host interactions determine viral entry and spreading. Here, we showed that neuropilin-1 (NRP1) significantly potentiates PRV infection. Mechanistically, NRP1 promoted PRV attachment and entry, and enhanced cell-to-cell fusion mediated by viral glycoprotein B (gB), gD, gH, and gL. Furthermore, through in vitro coimmunoprecipitation (Co-IP) and bimolecular fluorescence complementation (BiFC) assays, NRP1 was found to physically interact with gB, gD, and gH, and these interactions were C-end Rule (CendR) motif independent, in contrast to currently known viruses. Remarkably, we illustrated that the viral protein gB promotes NRP1 degradation via a lysosome-dependent pathway. We further demonstrate that gB promotes NRP1 degradation in a furin-cleavage-dependent manner. Interestingly, in this study, we generated gB furin cleavage site (FCS)-knockout PRV (Δfurin PRV) and evaluated its pathogenesis; in vivo, we found that Δfurin PRV virulence was significantly attenuated in mice. Together, our findings demonstrated that NRP1 is an important host factor for PRV and that NRP1 may be a potential target for antiviral intervention. IMPORTANCE Recent studies have shown accelerated PRV cross-species spillover and that PRV poses a potential threat to humans. PRV infection in humans always manifests as a high fever, tonic-clonic seizures, and encephalitis. Therefore, understanding the interaction between PRV and host factors may contribute to the development of new antiviral strategies against PRV. NRP1 has been demonstrated to be a receptor for several viruses that harbor CendR, including SARS-CoV-2. However, the relationships between NRP1 and PRV are poorly understood. Here, we found that NRP1 significantly potentiated PRV infection by promoting PRV attachment and enhanced cell-to-cell fusion. For the first time, we demonstrated that gB promotes NRP1 degradation via a lysosome-dependent pathway. Last, in vivo, Δfurin PRV virulence was significantly attenuated in mice. Therefore, NRP1 is an important host factor for PRV, and NRP1 may be a potential target for antiviral drug development.

Participatory approaches to delivering clinical sexually transmitted infections services: a narrative review.

Although sexual health programming and clinical sexually transmitted infections (STIs) services have traditionally been developed through 'top-down' approaches, there is emerging evidence that participatory approaches benefit the development and implementation of such services. Although other studies have already highlighted the benefits of participation in research and implementation of clinical STIs services delivery, this narrative review focuses on how community participation in clinical STIs services delivery has been operationalised and on the various aspects of clinical STIs services delivery in which participatory processes have been implemented. A PubMed search was conducted in January 2022 using the search terms that reflected the topic of participatory processes in clinical STIs services delivery to identify relevant papers. Only peer-reviewed papers published in English were reviewed, and no timeframe was selected. After reviewing existing studies, we identified how community participation has been incorporated across stages of clinical STIs service delivery, including planning, developing, delivering, evaluating, and scaling up, as well as gaps and challenges faced in implementing such approaches. This review highlighted how a wide range of participatory processes characterised by varying depths of participation have been used in the above processes. Challenges such as funding, socio-cultural barriers, technical barriers and the digital divide, issues of quality assurance, and standardising the measurement of participation remain, which may impede the uptake of participatory processes in clinical STIs services.

SARS-CoV-2 hijacks macropinocytosis to facilitate its entry and promote viral spike-mediated cell-to-cell fusion.

Revealing the mechanisms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry and cell-to-cell spread might provide insights for understanding the underlying mechanisms of viral pathogenesis, tropism, and virulence. The signaling pathways involved in SARS-CoV-2 entry and viral spike-mediated cell-to-cell fusion remain elusive. In the current study, we found that macropinocytosis inhibitors significantly suppressed SARS-CoV-2 infection at both the entry and viral spike-mediated cell-to-cell fusion steps. We demonstrated that SARS-CoV-2 entry required the small GTPase Rac1 and its effector kinase p21-activated kinase 1 by dominant-negative and RNAi assays in human embryonic kidney 293T-angiotensin-converting enzyme 2 cells and that the serine protease transmembrane serine protease 2 reversed the decrease in SARS-CoV-2 entry caused by the macropinocytosis inhibitors. Moreover, in the cell-to-cell fusion assay, we confirmed that macropinocytosis inhibitors significantly decreased viral spike-mediated cell-to-cell fusion. Overall, we provided evidence that SARS-CoV-2 utilizes a macropinocytosis pathway to enter target cells and to efficiently promote viral spike-mediated cell-to-cell fusion.

Robust humoral and cellular immune responses in long-term convalescent COVID-19 individuals following one-dose SARS-CoV-2 inactivated vaccination

COVID-19, caused by SARS-CoV-2, has resulted in hundreds of millions of infections and millions of deaths worldwide. Preliminary results exhibited excellent efficacy of SARS-CoV-2 vaccine in preventing hospitalization and severe disease. However, data on inactivated vaccine-induced immune responses of naturally infected patients are limited. Here, we characterized SARS-CoV-2 RBD-specific IgG (anti-S-RBD IgG) and neutralizing antibodies (NAbs) against SARS-CoV-2 wild type and variants of concerns (VOCs), as well as RBD-specific IgG-secreting B cells and antigen-specific T cells respectively in 51 SARS-CoV-2 recovered subjects and 63 healthy individuals. In SARS-CoV-2 recovered patients, a single dose vaccine is sufficient to reactivate robust anti-S-RBD IgG and NAbs. The neutralizing capacity against VOCs increased significantly post-vaccination no matter healthy individuals or SARS-CoV-2 recovered patients. In addition, RBD-specific IgG-secreting B cells in SARS-CoV-2 recovered patients were significantly higher than that in healthy vaccine recipients. After the vaccine booster, the frequencies of specific IFN-γ+ CD4+ T cell, IL-2+ CD4+ T cell, and TNF-α+ CD4+ T cell responses were significantly increased in SARS-CoV-2 recovered patients. Our data highlighted the safety and utility of SARS-CoV-2 inactivated vaccine and demonstrated that robust humoral and cellular immune response can be reactivated by one-dose inactivated vaccine in SARS-CoV-2 recovered patients.

Timeliness of health science popularization in local media of Shanghai during the outbreak of Coronavirus disease 2019

Objective: To analyze the timeliness of health science popularization during the outbreak of coronavirus disease 2019 (COVID-19) and its correlation with the epidemic situation and policies.

Mitigation of SARS-CoV-2 transmission at a large public university.

In Fall 2020, universities saw extensive transmission of SARS-CoV-2 among their populations, threatening health of the university and surrounding communities, and viability of in-person instruction. Here we report a case study at the University of Illinois at Urbana-Champaign, where a multimodal "SHIELD: Target, Test, and Tell" program, with other non-pharmaceutical interventions, was employed to keep classrooms and laboratories open. The program included epidemiological modeling and surveillance, fast/frequent testing using a novel low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD, and digital tools for communication and compliance. In Fall 2020, we performed >1,000,000 covidSHIELD tests, positivity rates remained low, we had zero COVID-19-related hospitalizations or deaths amongst our university community, and mortality in the surrounding Champaign County was reduced more than 4-fold relative to expected. This case study shows that fast/frequent testing and other interventions mitigated transmission of SARS-CoV-2 at a large public university.

Resource Allocation Equity in the China's Rural Three-Tier Healthcare System.

The rural three-tier healthcare system is an essential part of the Chinese healthcare service system. To ensure rural residents' equal access to such healthcare services, it is necessary to examine the current status of the healthcare system in rural China and formulate corresponding improvement suggestions. This study therefore collects the data from the China Health Statistics Yearbook, the China Health Yearbook and the China Statistical Yearbook between the years 2004 and 2021 to calculate the Gini coefficient (G), health resource density index (HRDI) and Theil index (T) first, and then perform the Mann-Kendall test afterwards to evaluate the equity of healthcare resource allocation comprehensively. This series of analysis helps in drawing the following conclusions: (1) county and county-level city medical and health institutions (CMHIs) show a higher development trend in comparison with township hospitals (THs) and village clinics (VCs); (2) VCs have higher institutional fairness, while for beds and personnel, CMHIs and THs are more fairly positioned; (3) more specifically for CMHIs and THs, personnel allocation is more fair than beds and institution allocations; (4) the density of healthcare resources in the eastern and central regions is higher than that in the western part, while the intra-regional distribution of beds and personnel in the west and central regions is better than that in the eastern region; (5) intra-regional differences are more significant than inter-regional differences and the fairness according to population distribution is higher than that of geographical area allocation. The results of this study provide theoretical basis for further optimizing the allocation of healthcare resources and improving the fairness of healthcare resources allocation from a macro perspective.

Improved drug-target interaction prediction with intermolecular graph transformer.

The identification of active binding drugs for target proteins (referred to as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.