BackgroundPrevious Caucasian studies have described venous thromboembolism in pregnancy; however, little is known about its incidence during pregnancy and early postpartum period in the Chinese population. We investigated the risk of venous thromboembolism in a “real-world” cohort of pregnant Chinese women with no prior history of venous thromboembolism.MethodsIn this observational study, 15,325 pregnancies were identified in 14,162 Chinese women at Queen Mary Hospital, Hong Kong between January 2004 and September 2016. Demographic data, obstetric information, and laboratory and imaging data were retrieved and reviewed.ResultsThe mean age at pregnancy was 32.4±5.3 years, and the median age was 33 years (interquartile range, 29–36 yr). Pre-existing or newly diagnosed diabetes mellitus was present in 627 women (4.1%); 359 (0.7%) women had pre-existing or newly detected hypertension. There was a small number of women with pre-existing heart disease and/or rheumatic conditions. Most deliveries (86.0%) were normal vaginal; the remaining were Cesarean section 2,146 (14.0%). The incidence of venous thromboembolism was 0.4 per 1,000 pregnancies, of which 83.3% were deep vein thrombosis and 16.7% were pulmonary embolism. In contrast to previous studies, 66.7% of venous thrombosis occurred in the first trimester.ConclusionChinese women had a substantially lower risk of venous thromboembolism during pregnancy and the postpartum period compared to that of Caucasians. The occurrence of pregnancy-related venous thromboembolism was largely confined to the early pregnancy period, probably related to the adoption of thromboprophylaxis, a lower rate of Cesarean section, and early mobilization.

The composition of the latent HIV-1 reservoir is shaped by when proviruses integrated into host genomes. These integration dates can be estimated by phylogenetic methods like root-to-tip (RTT) regression. However, RTT does not accommodate variation in the number of mutations over time, uncertainty in estimating the molecular clock or the position of the root in the tree. To address these limitations, we implemented a Bayesian extension of RTT as an R package (bayroot), which enables the user to incorporate prior information about the time of infection and start of antiretroviral therapy. Taking an unrooted maximum likelihood tree as input, we use a Metropolis-Hastings algorithm to sample from the joint posterior distribution of three parameters (the rate of sequence evolution, i.e., molecular clock; the location of the root; and the time associated with the root). Next, we apply rejection sampling to this posterior sample of model parameters to simulate integration dates for HIV proviral sequences. To validate this method, we use the R package treeswithintrees (twt) to simulate time-scaled trees relating samples of actively- and latently-infected T cells from a single host. We find that bayroot yields significantly more accurate estimates of integration dates than conventional RTT under a range of model settings.

Phylogenetics has played a pivotal role in the genomic epidemiology of severe acute respiratory syndrome coronavirus 2, such as tracking the emergence and global spread of variants and scientific communication. However, the rapid accumulation of genomic data from around the world—with over two million genomes currently available in the Global Initiative on Sharing All Influenza Data database—is testing the limits of standard phylogenetic methods. Here, we describe a new approach to rapidly analyze and visualize large numbers of SARS-CoV-2 genomes. Using Python, genomes are filtered for problematic sites, incomplete coverage, and excessive divergence from a strict molecular clock. All differences from the reference genome, including indels, are extracted using minimap2 and compactly stored as a set of features for each genome. For each Pango lineage (https://cov-lineages.org), we collapse genomes with identical features into ‘variants’, generate 100 bootstrap samples of the feature set union to generate weights, and compute the symmetric differences between the weighted feature sets for every pair of variants. The resulting distance matrices are used to generate neighbor-joining trees in RapidNJ that are converted into a majority-rule consensus tree for each lineage. Branches with support values below 50 per cent or mean lengths below 0.5 differences are collapsed, and tip labels on affected branches are mapped to internal nodes as directly sampled ancestral variants. Currently, we process about 2 million genomes in approximately 9 h on 52 cores. The resulting trees are visualized using the JavaScript framework D3.js as ‘beadplots’, in which variants are represented by horizontal line segments, annotated with beads representing samples by collection date. Variants are linked by vertical edges to represent branches in the consensus tree. These visualizations are published at https://filogeneti.ca/CoVizu. All source code was released under an MIT license at https://github.com/PoonLab/covizu.

Temporomandibular disorders (TMDs) are common and affect the temporomandibular joint (TMJ) and surrounding musculoskeletal tissues. Although traditional rehabilitative treatments such as physiotherapy, occlusal splints, orthodontics, and electrotherapy effectively manage TMDs, chiropractic therapy is emerging as a promising non-invasive treatment option. We report a 39-year-old female patient with TMD who underwent chiropractic therapy, including spinal adjustments, soft tissue therapy, and exercise rehabilitation. After four weeks of treatment, the patient reported a complete resolution of symptoms and an improved quality of life score. Thereafter, the patient continued chiropractic treatment monthly for six months, during which she reported no symptoms and demonstrated improvements in her spinal range of motion, open-mouth anatomy, and cervical lordosis. This case study highlights the efficacy of applying an interdisciplinary approach to treating TMD and the potential of chiropractic therapy as a valuable treatment option for managing TMD.