Water Crisis in Vietnam

• Immediately after the Vietnam War ended in 1975, Vietnam experienced economic turmoil and famine as the roots of industrialization began to grip the nation. • In 1986, the government declared a rapid transition from a planned to a market economy would take hold. The ensuing change caused further increased industrial development and a subsequent growth of the emerging market economy. 1 • To this day, Vietnam’s GDP is rising yearly at a rapid rate. • For this reason, much of Vietnam has been developed in a relatively short amount of time (since the end of the war) but much of it has lagged behind, including the infrastructure including water pipes and water sanitation plants. This lag has caused limited access to sanitized water in both rural and urban areas. • Despite an overall adequate water access for Vietnamese citizens, the sanitation of supplied water has not improved as markedly as the country as a whole. • Sanitation has increased from 37% in 1990 to 75% in 2011 as defined by the JMP’s sanitation standards. Here, sanitation is defined as the distance between a water supply and human excretion.2 • Although Vietnam’s water has been made safer over the past few decades, it is largely undrinkable. • A 2009 study done by scientists at the Vietnam Institute of Biotechnology concluded that ammonia levels in Vietnam’s waters range from an average of 6-18 times higher than an acceptable level. 3 Furthermore, arsenic levels range from 2-3 times higher than an acceptable level.https://jdc.jefferson.edu/cwicposters/1025/thumbnail.jp

Relationship of Blood Lactate and Sweat Lactate on Exercise Intensity

Typical procedures for measuring blood lactate involve either finger stick blood samples or venous blood draws. The literature is equivocal regarding whether sweat lactate values change with exercise intensity. Recently, wearable technology devices have been developed to measure sweat lactate. Purpose: To examine the relationship between sweat lactate and blood lactate values during incremental exercise. Methods: This study consisted of 12 (8 male, 4 female) healthy recreationally active individuals (VO2peak 35.5 ± 7.6 ml/kg/min) between the ages of 18 and 25 (22 ± 2 yrs) who volunteered for the study. Participants performed an exercise test on a cycle ergometer to volitional fatigue to determine blood lactate, lactate threshold, VO2peak, and peak heart rate (HR). Blood lactate was collected via finger stick at each 3-min stage of exercise. Participants performed a subsequent exercise session at 40, 60, and 80% heart rate reserve (HRR). During the 20-min stages of this test, blood and sweat lactate were collected during each intensity level. Sweat lactate was collected in a sweat “pouch” at each state of exercise. Sweat lactate samples were analyzed via the lactate oxidase method on a Chemwell 2910 chemistry analyzer. Blood lactate samples were analyzed using a Lactate Plus analyzer. Whole body sweat rate was calculated from pre- and post-exercise body weight at each intensity, factoring in water consumed and urine voided. Results: Sweat rate increased with increasing intensity (40%: 9.66 ± 7.58; 60%: 18.10 ± 12.51; 80% 24.32 ± 15.44 ml/min). Sweat lactate significantly differed between 60 and 80% intensities (15.66 ± 5.73, 12.52 ± 4.44 mmol/L, respectively), P = 0.03. Blood lactate levels at 40, 60, and 80% intensities were 2.67 ± 1.15, 3.60 ± 1.90, and 4.83 ± 1.52, respectively (P \u3c 0.001). CONCLUSION: These findings agree with Buono, Lee, & Miller, 2010 who found sweat lactate decreases as sweat rate increases. It is likely that sweat lactate decreases with increasing exercise intensity due to dilution as sweat rate increases. From this data, it appears that sweat lactate does not demonstrate a relationship with blood lactate that warrants replacing blood lactate in exercise testing with sweat lactate. This may be due to the lactate in sweat originating from eccrine glands and thus is not reflective of muscle metabolism

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

A randomised controlled study of Bronchoscopic Lung Volume Reduction with endobronchial valves for patients with Heterogeneous emphysema and Intact interlobar Fissures: the BeLieVeR-HIFi study

Background: Despite optimal therapy many patients with emphysema remain significantly breathless and limited. Bronchoscopic lung volume reduction (BLVR), using valves placed to allow air to leave but not enter the worst-affected areas of the lung, has been proposed as a way to improve lung function in these patients, but response is variable because interlobar collateral ventilation prevents the devices from working. Based on retrospective analysis of clinical trials, patients with heterogeneous emphysema and intact interlobar fissures are most likely to benefit. Objectives: To establish whether or not it is possible to identify patients prospectively who will reliably benefit from endobronchial valve placement. Design: Prospective, randomised, parallel-group, double-blind, sham-controlled trial. Setting: The study was performed at a single specialist centre. Participants: Adult patients with heterogeneous emphysema and a target lobe with intact interlobar fissures were eligible if they had significant gas trapping (total lung capacity > 100% predicted, residual volume > 150% predicted), breathlessness [Medical Research Council (MRC) dyspnoea score of ≥ 3] and exercise limitation (6-minute walk distance of < 450 m). Participants were on optimised pharmacotherapy and were non-smokers. Interventions: Study participants were randomised to either unilateral lobar endobronchial valve placement aiming to achieve lobar atelectasis or bronchoscopy and ‘sham’ valve placement. Main outcome measures: The primary end point was improvement in forced expiratory volume in 1 second (FEV1) in the treatment arm compared with the control arm measured 90 days post procedure. Secondary end points were change in lung volumes, gas transfer, exercise capacity (both walking and endurance cycle ergometry) and health-related quality of life. Results: In total, 50 patients were recruited, 25 to each arm; 62% were male and mean (standard deviation) FEV1% predicted was 31.7% (10.2%). The primary end point of the study was met as FEV1 increased by 24.8% [95% confidence interval (CI) 8.0% to 41.5%] in the treatment arm and by 3.9% (95% CI 0.7% to 7.1%) in the control arm [between-group difference 20.9% (95% CI 4.3% to 37.5%); p = 0.033]. There were both statistically and clinically significant improvements in lung volumes and carbon monoxide gas transfer as well as endurance time and dynamic hyperinflation during cycle ergometry. Two deaths occurred in the treatment arm and one control patient was unable to attend for follow-up assessment because of a prolonged pneumothorax. Two pneumothoraces occurred in the treatment arm. Conclusions: With appropriate selection of patients through a multidisciplinary team it is possible to produce a significant improvement in lung function through lobar occlusion with endobronchial valves in heterogeneous emphysema. Prospective trials are needed to compare the effect of BLVR with surgical approaches in terms of magnitude and duration of benefit. Trial registration: Current Controlled Trials ISRCTN04761234. Funding: This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and NIHR partnership

Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer.

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients

Segmentation of joint and musculoskeletal tissue in the study of arthritis

As the most frequent cause of physical disability, musculoskeletal diseases such as arthritis and osteoporosis have a great social and economical impact. Quantitative magnetic resonance imaging (MRI) biomarkers are important tools that allow clinicians to better characterize, monitor, and even predict musculoskeletal disease progression. Post-processing pipelines often include image segmentation. Manually identifying the border of the region of interest (ROI) is a difficult and time-consuming task. Manual segmentation is also affected by inter- and intrauser variability, thus limiting standardization. Fully automatic or semi-automatic methods that minimize the user interaction are highly desirable. Unfortunately, an ultimate, highly reliable and extensively evaluated solution for joint and musculoskeletal tissue segmentation has not yet been proposed, and many clinical studies still adopt fully manual procedures. Moreover, the clinical translation of several promising quantitative MRI techniques is highly affected by the lack of an established, fast, and accurate segmentation method. The goal of this review is to present some of the techniques proposed in recent literature that have been adopted in clinical studies for joint and musculoskeletal tissue analyses in arthritis patients. The most widely used MRI sequences and image processing algorithms employed to accomplish segmentation challenges will be discussed in this paper