The Effect of the COVID-19 Pandemic on Non-COVID-19 Deaths: Population-Wide Retrospective Cohort Study.

BACKGROUND: Health care avoidance in the COVID-19 pandemic has been widely reported. Yet few studies have investigated the dynamics of hospital avoidance behavior during pandemic waves and inferred its impact on excess non-COVID-19 deaths. OBJECTIVE: This study aimed to measure the impact of hospital avoidance on excess non-COVID-19 deaths in public hospitals in Hong Kong. METHODS: This was a retrospective cohort study involving 11,966,786 patients examined between January 1, 2016, and December 31, 2021, in Hong Kong. All data were linked to service, treatment, and outcomes. To estimate excess mortality, the 2-stage least squares method was used with daily tallies of emergency department (ED) visits and 28-day mortality. Records for older people were categorized by long-term care (LTC) home status, and comorbidities were used to explain the demographic and clinical attributes of excess 28-day mortality. The primary outcome was actual excess death in 2020 and 2021. The 2-stage least squares method was used to estimate the daily excess 28-day mortality by daily reduced visits. RESULTS: Compared with the prepandemic (2016-2019) average, there was a reduction in total ED visits in 2020 of 25.4% (548,116/2,142,609). During the same period, the 28-day mortality of non-COVID-19 ED deaths increased by 7.82% (2689/34,370) compared with 2016-2019. The actual excess deaths in 2020 and 2021 were 3143 and 4013, respectively. The estimated total excess non-COVID-19 28-day deaths among older people in 2020 to 2021 were 1958 (95% CI 1100-2820; no time lag). Deaths on arrival (DOAs) or deaths before arrival (DBAs) increased by 33.6% (1457/4336) in 2020, while non-DOA/DBAs increased only by a moderate 4.97% (1202/24,204). In both types of deaths, the increases were higher during wave periods than in nonwave periods. Moreover, non-LTC patients saw a greater reduction in ED visits than LTC patients across all waves, by more than 10% (non-LTC: 93,896/363,879, 25.8%; LTC: 7,956/67,090, 11.9%). Most of the comorbidity subsets demonstrated an annualized reduction in visits in 2020. Renal diseases and severe liver diseases saw notable increases in deaths. CONCLUSIONS: We demonstrated a statistical method to estimate hospital avoidance behavior during a pandemic and quantified the consequent excess 28-day mortality with a focus on older people, who had high frequencies of ED visits and deaths. This study serves as an informed alert and possible investigational guideline for health care professionals for hospital avoidance behavior and its consequences.

An artificial intelligence-enabled smartphone app for real-time pressure injury assessment.

The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%-100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.

Incidence of Emergency Department Visits for Sexual Abuse Among Youth in Hong Kong Before and During the COVID-19 Pandemic.

This cohort study assesses the incidence of emergency department (ED) visits in Hong Kong, China, for sexual abuse among youth before and during the COVID-19 pandemic.

The method to quantify cell elasticity based on the precise measurement of pressure inducing cell deformation in microfluidic channels.

The cell elasticity has attracted extensive research interests since it not only provides new insights into cell biology but also is an emerging mechanical marker for the diagnosis of some diseases. This paper reports the method for the precise measurement of mechanical properties of single cells deformed to a large extent using a novel microfluidic system integrated with a pressure feedback system and small particle separation unit. The particle separation system was employed to avoid the blockage of the cell deformation channel to enhance the measurement throughput. This system is of remarkable application potential in the precise evaluation of cell mechanical properties. In brief, this paper reports:•The manufacturing of the chip using standard soft lithography;•The methods to deform single cells in a microchannel and measure the relevant pressure drop using a pressure sensor connecting to the microfluidic chip;•Calculation of the mechanical properties including stiffness and fluidity of each cell based on a power-law rheology model describing the viscoelastic behaviors of cells;•Automatic and real-time measurement of the mechanical properties using video processing software.

Advancements in Host-Based Interventions for Influenza Treatment.

Influenza is a major acute respiratory infection that causes mortality and morbidity worldwide. Two classes of conventional antivirals, M2 ion channel blockers and neuraminidase inhibitors, are mainstays in managing influenza disease to lessen symptoms while minimizing hospitalization and death in patients with severe influenza. However, the development of viral resistance to both drug classes has become a major public health concern. Vaccines are prophylaxis mainstays but are limited in efficacy due to the difficulty in matching predicted dominant viral strains to circulating strains. As such, other potential interventions are being explored. Since viruses rely on host cellular functions to replicate, recent therapeutic developments focus on targeting host factors involved in virus replication. Besides controlling virus replication, potential targets for drug development include controlling virus-induced host immune responses such as the recently suggested involvement of innate lymphoid cells and NADPH oxidases in influenza virus pathogenesis and immune cell metabolism. In this review, we will discuss the advancements in novel host-based interventions for treating influenza disease.

Recognition of Double-Stranded RNA and Regulation of Interferon Pathway by Toll-Like Receptor 10.

Toll-like receptor (TLR)-10 remains an orphan receptor without well-characterized ligands or functions. Here, we reveal that TLR10 is predominantly localized to endosomes and binds dsRNA in vitro at endosomal pH, suggesting that dsRNA is a ligand of TLR10. Recognition of dsRNA by TLR10 activates recruitment of myeloid differentiation primary response gene 88 for signal transduction and suppression of interferon regulatory factor-7 dependent type I IFN production. We also demonstrate crosstalk between TLR10 and TLR3, as they compete with each other for dsRNA binding. Our results suggest for the first time that dsRNA is a ligand for TLR10 and propose novel dual functions of TLR10 in regulating IFN signaling: first, recognition of dsRNA as a nucleotide-sensing receptor and second, sequestration of dsRNA from TLR3 to inhibit TLR3 signaling in response to dsRNA stimulation.

Toll-like receptor 10 is involved in induction of innate immune responses to influenza virus infection.

Toll-like receptors (TLRs) play key roles in innate immune recognition of pathogen-associated molecular patterns of invading microbes. Among the 10 TLR family members identified in humans, TLR10 remains an orphan receptor without known agonist or function. TLR10 is a pseudogene in mice and mouse models are noninformative in this regard. Using influenza virus infection in primary human peripheral blood monocyte-derived macrophages and a human monocytic cell line, we now provide previously unidentified evidence that TLR10 plays a role in innate immune responses following viral infection. Influenza virus infection increased TLR10 expression and TLR10 contributed to innate immune sensing of viral infection leading to cytokine induction, including proinflammatory cytokines and interferons. TLR10 induction is more pronounced following infection with highly pathogenic avian influenza H5N1 virus compared with a low pathogenic H1N1 virus. Induction of TLR10 by virus infection requires active virus replication and de novo protein synthesis. Culture supernatants of virus-infected cells modestly up-regulate TLR10 expression in nonvirus-infected cells. Signaling via TLR10 was activated by the functional RNA-protein complex of influenza virus leading to robust induction of cytokine expression. Taken together, our findings identify TLR10 as an important innate immune sensor of viral infection and its role in innate immune defense and immunopathology following viral and bacterial pathogens deserves attention.