Hemoglobin A1c in early pregnancy to identify preexisting diabetes mellitus and women at risk of hyperglycemic pregnancy complications.

BACKGROUND: Unrecognized diabetes mellitus during pregnancy could pose serious maternal and neonatal complications. A hemoglobin A1c level of ≥6.5% was used to diagnose both diabetes mellitus in nonpregnant individuals and diabetes in pregnancy. As the hemoglobin A1c level could be influenced by maternal physiological changes, the optimal cutoff in early pregnancy to detect women with diabetes in pregnancy and associated complications remains unclear. OBJECTIVE: This study aimed to evaluate the diagnostic performance of various hemoglobin A1c levels and the optimal hemoglobin A1c cutoff to identify mothers with diabetes in pregnancy diagnosed by the gold standard 75 g oral glucose tolerance test before 24 weeks of gestation. In addition, the pregnancy and neonatal outcomes were compared using the optimal hemoglobin A1c cutoff. STUDY DESIGN: A retrospective cohort study was conducted between 2004 and 2019. Women with at least 1 risk factor of gestational diabetes mellitus received an oral glucose tolerance test before 24 weeks of gestation. Terminology of hyperglycemia first detected during pregnancy by oral glucose tolerance test was classified as either diabetes in pregnancy or gestational diabetes mellitus following the World Health Organization's recommendation. Women who met the diagnostic criteria of diabetes in pregnancy and early-onset gestational diabetes mellitus (ie, before 24 weeks of gestation) and had a paired hemoglobin A1c measurement within 4 weeks of their early oral glucose tolerance test were studied. Sensitivity, specificity, and positive and negative predictive values at various hemoglobin A1c cutoffs were calculated for the detection of diabetes in pregnancy. The optimal hemoglobin A1c level was identified from the constructed receiver operating characteristic curves. Multivariate binary logistic regression analyses were performed to calculate the unadjusted and adjusted odds ratios for pregnancy complications. RESULTS: There were 63,111 deliveries, and 22,949 women underwent an oral glucose tolerance test before 24 weeks of gestation. A total of 157 and 3210 women met the diagnostic criteria of diabetes in pregnancy and early-onset gestational diabetes mellitus using an oral glucose tolerance test, respectively. Only 346 participants had a paired hemoglobin A1c and oral glucose tolerance test measurement (82 cases with diabetes in pregnancy and 264 cases with early-onset gestational diabetes mellitus). The receiver operating characteristic curve identified an optimal hemoglobin A1c cutoff of 5.7% to diagnose diabetes in pregnancy, with a sensitivity of 64.6%, specificity of 81.1%, positive predictive value of 51.5%, and negative predictive value of 88.1%. A hemoglobin A1c cutoff of either 5.9% or 6.5% could miss 47.6% or 73.2% of women with diabetes in pregnancy. In multivariate logistic regression analysis, a hemoglobin A1c level of ≥5.7% increased the risk of maternal insulin use (adjusted odds ratio, 6.69; 95% confidence interval, 3.44-12.99), macrosomia (adjusted odds ratio, 7.43; 95% confidence interval, 1.90-29.00), and shoulder dystocia (adjusted odds ratio, 6.56; 95% confidence interval, 1.161-37.03). CONCLUSION: The optimal hemoglobin A1c cutoff to detect diabetes in pregnancy diagnosed using an oral glucose tolerance test before 24 weeks of gestation was 5.7%, but this cutoff could not reliably identify diabetes in pregnancy owing to the low sensitivity. However, an early hemoglobin A1c level of ≥5.7% indicated increased risks of pregnancy and neonatal complications.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Discovery of antibiotics that selectively kill metabolically dormant bacteria.

There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.

Allan-Herndon-Dudley syndrome in Hong Kong: Implication for newborn screening.

BACKGROUND: Allan-Herndon-Dudley syndrome (MCT 8 deficiency) is an X-linked recessive condition caused by hemizygous pathogenic variants in SLC16A2 encoding the monocarboxylate transporter 8 (MCT8). Patients present with global developmental delay and neurological impairment, and abnormal serum thyroid function tests. The drug, 3,3',5 triiodothyroacetic acid (TRIAC), was recently demonstrated to improve the endocrinological profile. Improvement in diagnostic approach is key to earlier start of treatment. PATIENT FINDINGS: We described four Chinese patients with MCT8 deficiency undergoing different diagnostic odysseys. Their initial presentation included global developmental delay and dystonia. Patient 2 also had epilepsy. Patients 1 and 2 presented with two novel variants: (1)hemizygous NM_006517.4(SLC16A2):c.1170 + 2 T > A; p.(?), and (2)hemizygous NM_006517.4(SLC16A2):c.305dupT; p.(Val103GlyfsTer17) respectively. Patients 3 and 4 were biological brothers harboring hemizygous NM_006517.4(SLC16A2):c.305dupT; p.(Val103GlyfsTer17), which was first reported in 2004. We obtained the measurement of triiodothyronine (T3) and reverse T3 (rT3) from dried blood spot samples collected on Day 1 of life from Patient 1 and studied the biomarkers (rT3 and T3/rT3 ratio) proposed by Iwayama et al. for the detection of MCT8 deficiency at birth. Our data verified the significantly reduced rT3 level in Patient 1, compared with healthy newborns, although low T3 level and comparable T3/rT3 ratio with controls were detected. SUMMARY: Patients with MCT8 deficiency often undergo diagnostic odysseys. An early diagnosis could be missed by a normal newborn thyroid function screening result based on biochemical measurement of TSH and/or T4/fT4. Early detection of rT3 is key to improving current diagnostic approach. CONCLUSION: We recommend that full thyroid function profile (TSH, T4/fT4, T3/fT3, rT3) be considered early for all pediatric patients presenting with unexplained developmental delay and/or dystonia. The potential inclusion of rT3 measurement in newborn screening may prove promising.

Synthetic control of actin polymerization and symmetry breaking in active protocells.

Non-linear biomolecular interactions on the membranes drive membrane remodeling that underlies fundamental biological processes including chemotaxis, cytokinesis, and endocytosis. The multitude of biomolecules, the redundancy in their interactions, and the importance of spatiotemporal context in membrane organization hampers understanding the physical principles governing membrane mechanics. A minimal, in vitro system that models the functional interactions between molecular signaling and membrane remodeling, while remaining faithful to cellular physiology and geometry is powerful yet remains unachieved. Here, inspired by the biophysical processes underpinning chemotaxis, we reconstituted externally-controlled actin polymerization inside giant unilamellar vesicles, guiding self-organization on the membrane. We show that applying undirected external chemical inputs to this system results in directed actin polymerization and membrane deformation that are uncorrelated with upstream biochemical cues, indicating symmetry breaking. A biophysical model of the dynamics and mechanics of both actin polymerization and membrane shape suggests that inhomogeneous distributions of actin generate membrane shape deformations in a non-linear fashion, a prediction consistent with experimental measurements and subsequent local perturbations. The active protocellular system demonstrates the interplay between actin dynamics and membrane shape in a symmetry breaking context that is relevant to chemotaxis and a suite of other biological processes.

Leveraging artificial intelligence in the fight against infectious diseases.

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences.

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.

Discovering small-molecule senolytics with deep neural networks.

The accumulation of senescent cells is associated with aging, inflammation and cellular dysfunction. Senolytic drugs can alleviate age-related comorbidities by selectively killing senescent cells. Here we screened 2,352 compounds for senolytic activity in a model of etoposide-induced senescence and trained graph neural networks to predict the senolytic activities of >800,000 molecules. Our approach enriched for structurally diverse compounds with senolytic activity; of these, three drug-like compounds selectively target senescent cells across different senescence models, with more favorable medicinal chemistry properties than, and selectivity comparable to, those of a known senolytic, ABT-737. Molecular docking simulations of compound binding to several senolytic protein targets, combined with time-resolved fluorescence energy transfer experiments, indicate that these compounds act in part by inhibiting Bcl-2, a regulator of cellular apoptosis. We tested one compound, BRD-K56819078, in aged mice and found that it significantly decreased senescent cell burden and mRNA expression of senescence-associated genes in the kidneys. Our findings underscore the promise of leveraging deep learning to discover senotherapeutics.

Predicting progression-free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development.

Background: Advanced head and neck squamous cell carcinoma (HNSCC) is associated with a poor prognosis, and biomarkers that predict response to treatment are highly desirable. The primary aim was to predict progression-free survival (PFS) with a multivariate risk prediction model. Methods: Experimental covariates were derived from blood samples of 56 HNSCC patients which were prospectively obtained within a Phase 2 clinical trial (NCT02633800) at baseline and after the first treatment cycle of combined platinum-based chemotherapy with cetuximab treatment. Clinical and experimental covariates were selected by Bayesian multivariate regression to form risk scores to predict PFS. Results: A 'baseline' and a 'combined' risk prediction model were generated, each of which featuring clinical and experimental covariates. The baseline risk signature has three covariates and was strongly driven by baseline percentage of CD33+CD14+HLADRhigh monocytes. The combined signature has six covariates, also featuring baseline CD33+CD14+HLADRhigh monocytes but is strongly driven by on-treatment relative change of CD8+ central memory T cells percentages. The combined model has a higher predictive power than the baseline model and was successfully validated to predict therapeutic response in an independent cohort of nine patients from an additional Phase 2 trial (NCT03494322) assessing the addition of avelumab to cetuximab treatment in HNSCC. We identified tissue counterparts for the immune cells driving the models, using imaging mass cytometry, that specifically colocalized at the tissue level and correlated with outcome. Conclusions: This immune-based combined multimodality signature, obtained through longitudinal peripheral blood monitoring and validated in an independent cohort, presents a novel means of predicting response early on during the treatment course. Funding: Daiichi Sankyo Inc, Cancer Research UK, EU IMI2 IMMUCAN, UK Medical Research Council, European Research Council (335326), Merck Serono. Cancer Research Institute, National Institute for Health Research, Guy's and St Thomas' NHS Foundation Trust and The Institute of Cancer Research. Clinical trial number: NCT02633800.

Accuracy of Acuros[Formula: see text] BV as determined from GATE monte-carlo simulation.

The American Association of Physicists in Medicine's Task Group No.43 has provided a standardised dose calculation methodology that is now the international benchmark for all brachytherapy dosimetry publications and treatment planning systems. However, limitations in this methodology has seen the development of Model-Based Dose Calculation Algorithms (MBDCA). In 2009, Varian (Varian Medical Systems, Palo Alto, CA, USA) released Acuros[Formula: see text] BrachyVision (ABV) which calculates dose by explicitly solving the Linear Boltzmann Transport Equation. In this study we have assessed the accuracy of ABV dose calculations within a range of materials relevant to high dose rate brachytherapy with an iridium-192 ([Formula: see text]Ir) source. Accuracy assessment has been achieved by implementing a modelled GamaMed Plus [Formula: see text]Ir source within a series of phantoms using the GEANT4 Application for Emission Tomography (GATE) to calculate dose for comparison with dose as determined by ABV. Comparisons between GATE and ABV were made using point-to-point profile comparisons and 1D gamma analysis. Source validation results yielded good agreement with published data. Spectrum and TG43U1 comparisons showed no major differences, with TG43U1 comparisons agreeing within ± 1%. Point-to-point comparisons showed large differences between GATE and ABV near the source and in low density materials. 1D gamma analysis pass criteria of 2%/1 mm and 2%/2 mm yielded passing rates ranging between 51.72-100% and 62.07-100% respectively. A critical analysis of this study's results suggest that ABV is unable to accurately calculate doses in low density materials. Furthermore, spatial accuracy of dose near the source is within 2 mm.

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery.

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein-ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning-based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. This work indicates that advances in modeling protein-ligand interactions, particularly using machine learning-based approaches, are needed to better harness AlphaFold2 for drug discovery.

Reactive metabolic byproducts contribute to antibiotic lethality under anaerobic conditions.

Understanding how bactericidal antibiotics kill bacteria remains an open question. Previous work has proposed that primary drug-target corruption leads to increased energetic demands, resulting in the generation of reactive metabolic byproducts (RMBs), particularly reactive oxygen species, that contribute to antibiotic-induced cell death. Studies have challenged this hypothesis by pointing to antibiotic lethality under anaerobic conditions. Here, we show that treatment of Escherichia coli with bactericidal antibiotics under anaerobic conditions leads to changes in the intracellular concentrations of central carbon metabolites, as well as the production of RMBs, particularly reactive electrophilic species (RES). We show that antibiotic treatment results in DNA double-strand breaks and membrane damage and demonstrate that antibiotic lethality under anaerobic conditions can be decreased by RMB scavengers, which reduce RES accumulation and mitigate associated macromolecular damage. This work indicates that RMBs, generated in response to antibiotic-induced energetic demands, contribute in part to antibiotic lethality under anaerobic conditions.

Chinese normotensive and essential hypertensive reference intervals for plasma aldosterone and renin activity by liquid chromatography-tandem mass spectrometry.

OBJECTIVES: The renin-angiotensin-aldosterone system (RAAS) regulates blood pressure. Plasma renin activities (PRA) and plasma aldosterone concentrations (PAC) are biomarkers related to RAAS. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements for PRA and PAC have become popular. Method-specific reference intervals (RIs) are required. METHODS: Routine PRA and PAC services in a Hong Kong teaching hospital were based on LC-MS/MS methods. PRA and PAC RIs were developed for normotensive subjects and essential hypertensive (EH) patients. Healthy volunteers were recruited to establish normotensive RIs. PRA and PAC results of hypertensive patients with urine aldosterone tests for primary aldosteronism (PA) screening were retrieved from the laboratory information system. Patients without PA were included. Patients with secondary hypertension and patients on medications affecting the RAAS were excluded. The central 95% RIs were established based on the recommendations of the Clinical and Laboratory Standards Institute guideline C28-A3. RESULTS: PRA and PAC of 170 normotensive volunteers and 362 EH patients were analysed. There was no sex-specific difference in PRA and PAC for normotensive and EH reference subjects. Differences for PRA and PAC were noted between normotensive subjects aged below 45 and their older counterparts. However, such a difference was only identified for PRA but not PAC in EH patients. Age-specific RIs were established accordingly. CONCLUSIONS: This study presented age-specific LC-MS/MS RIs of PRA and PAC for both normotensive and EH populations for local Chinese in Hong Kong.

Clinical implementation of HyperArc.

The aim of this study was to present our experience on clinical implementation of HyperArc including dosimetric comparison between VMAT and HyperArc plans and dosimetric verification of HyperArc. In this study, eleven previously treated cases of brain metastasis were selected from our brain stereotactic radiotherapy program. The cases were retrospectively planned using HyperArc technique and the plan quality was evaluated. In addition, dosimetric effects of HyperArc plan with different energies and using jaw tracking technique were evaluated. Furthermore, dosimetric verification of HyperArc plans was performed using ion chamber and radiochromic film. Our results of dosimetric comparison shows that HyperArc technique improved both conformity index and gradient index compared to VMAT plans. We also found that using 6MV flattening filter free (6MV-FFF) beam improves gradient index in HyperArc plans compared to using 6MV flattening filter beam. Furthermore, our results show that jaw tracking technique reduces the size of low dose volume while maintaining similar target coverage, conformity index, and gradient index. In our dosimetric verification study, results of ion chamber and film measurement indicate no significant difference between VMAT and HyperArc plans. In conclusion, HyperArc simplifies planning of stereotactic treatment for brain and improves the dosimetry in treatment plans. Additionally, HyperArc provides for a safe and efficient treatment delivery system for stereotactic treatments to brain.

Increased energy demand from anabolic-catabolic processes drives ß-lactam antibiotic lethality.

ß-Lactam antibiotics disrupt the assembly of peptidoglycan (PG) within the bacterial cell wall by inhibiting the enzymatic activity of penicillin-binding proteins (PBPs). It was recently shown that ß-lactam treatment initializes a futile cycle of PG synthesis and degradation, highlighting major gaps in our understanding of the lethal effects of PBP inhibition by ß-lactam antibiotics. Here, we assess the downstream metabolic consequences of treatment of Escherichia coli with the ß-lactam mecillinam and show that lethality from PBP2 inhibition is a specific consequence of toxic metabolic shifts induced by energy demand from multiple catabolic and anabolic processes, including accelerated protein synthesis downstream of PG futile cycling. Resource allocation into these processes is coincident with alterations in ATP synthesis and utilization, as well as a broadly dysregulated cellular redox environment. These results indicate that the disruption of normal anabolic-catabolic homeostasis by PBP inhibition is an essential factor for ß-lactam antibiotic lethality.

Barriers to early detection and management of oral cancer in the Asia Pacific region.

OBJECTIVE: Oral cancer is amenable to early detection but remains a prominent cause of mortality in the Asia Pacific region. This study aimed to identify barriers to early detection and management of oral cancer in the Asia Pacific region. METHODS: A mixed-methods approach was employed triangulating findings from a survey and focus groups. The survey was conducted among seven representative members of the Asia Pacific Oral Cancer Network (APOCNET) across six countries. Focus groups were conducted to gain deeper insights into the findings of the survey. RESULTS: The identified barriers were a lack of national cancer control strategies and cancer registries and the limited availability of trained health care professionals. Overcoming these challenges in the Asia Pacific region where resources are scarce will require collaborative partnerships in data collection and novel approaches for continuous professional training including eLearning. Further, to overcome the lack of trained health care professionals, innovative approaches to the management of oral potentially malignant lesions and oral cancer including telemedicine were suggested. CONCLUSION: The findings of this study should be taken into account when charting national cancer control plans for oral cancer and will form the basis for future collaborative studies in evaluating effective measures to improve oral cancer detection and management in low- and middle-income countries.

Modified Lumbopelvic Technique Using S1 Pedicle Screws for Spinopelvic Dissociation U-Type and H-Type Sacral Fractures With Kyphotic Deformity.

SUMMARY: Spinopelvic dissociation injuries are complex injuries defined as discontinuity between the appendicular and axial skeleton. Fracture patterns are variable, but U-type and H-type fractures are common and often present with kyphotic deformity along with translational displacement and impaction. The ideal method of fixation has not been established for these injuries. The goals of treatment include restoration of alignment, stability, and neural decompression as needed. Traditional methods of lumbopelvic fixation have spanned the upper sacral fracture site. Our novel modified method of lumbopelvic fixation directly instruments the S1 body. This allows for direct manipulation of the fracture which we theorize improves reduction and increases stability across the fracture. This article characterizes the injury patterns, outlines the modified technique, and reports the clinical and radiographic outcomes of our modified lumbopelvic fixation technique and construct.

Preliminary study on ultrasound-guided high-intensity focused ultrasound ablation for treatment of broad ligament uterine fibroids.

OBJECTIVE: To investigate the feasibility, efficacy and safety of ultrasound-guided high-intensity focused ultrasound (HIFU) ablation for treating the broad ligament uterine fibroid (BLUF). METHODS: A total of 236 patients with symptomatic uterine fibroids were enrolled and treated with JC-200 extracorporeal ultrasound-guided HIFU under conscious sedation between January 2017 and December 2018. Of them, data of 12 patients with 13 broad ligament fibroids were retrospectively analyzed. The patients' mean age was 38.6 ± 6.3 years. The focused ultrasound target was deployed and moved from the deeper layer to the superficial layer of BLUFs. All patients underwent contrast-enhanced MRI (CE-MRI) before, immediate post-operation, and six months after the HIFU ablation procedure. The fibroid size, non-perfusion volume (NPV) ratio, the reduction of fibroid volumes, adverse events, symptom changes, and abnormal MRI findings associated with the HIFU treatment were analyzed. RESULTS: Ultrasound-guided HIFU ablation in the twelve patients was technically successful with one session treatment. The mean longest diameter of BLUFs was 6.2 ± 2.3 cm. The mean NPV ratio of fibroids was 84.08%± 9.4%. After HIFU ablation, lower abdominal pain occurred in 7 cases, sacrococcygeal pain in 3 cases, and mild skin pain in 6 cases. There were no severe adverse events and complications associated with the treatment. At 6 months post-treatment follow-up, the mean fibroid volume decreased by 56.2%± 9.0% (p < 0.05), and the symptoms related to broad ligament fibroids were improved or disappeared. CONCLUSIONS: Ultrasound-guided high-intensity focused ultrasound ablation is feasible, effective, and safe for treating broad ligament fibroids.

Understanding Beta-Lactam-Induced Lysis at the Single-Cell Level.

Mechanical rupture, or lysis, of the cytoplasmic membrane is a common cell death pathway in bacteria occurring in response to ß-lactam antibiotics. A better understanding of the cellular design principles governing the susceptibility and response of individual cells to lysis could indicate methods of potentiating ß-lactam antibiotics and clarify relevant aspects of cellular physiology. Here, we take a single-cell approach to bacterial cell lysis to examine three cellular features-turgor pressure, mechanosensitive channels, and cell shape changes-that are expected to modulate lysis. We develop a mechanical model of bacterial cell lysis and experimentally analyze the dynamics of lysis in hundreds of single Escherichia coli cells. We find that turgor pressure is the only factor, of these three cellular features, which robustly modulates lysis. We show that mechanosensitive channels do not modulate lysis due to insufficiently fast solute outflow, and that cell shape changes result in more severe cellular lesions but do not influence the dynamics of lysis. These results inform a single-cell view of bacterial cell lysis and underscore approaches of combatting antibiotic tolerance to ß-lactams aimed at targeting cellular turgor.