A Cry for Help. A New Letter from the Yale Collection

The new text is a private letter, in which the writer sends a cry for help describing himself as naked. I discuss the distinctive uses of γυμνός and show how this word changes from its literal sense into a more abstract meaning

First Principles Calculation of Field Emission from Nanostructures using Time-Dependent Density Functional Theory: a Simplified Approach

We introduce a new simplified method for computing the electron field emission current in short carbon nanotubes using ab-initio computation in periodic simulation cells. We computed the evolution of the wave functions using Time-Dependent Density Functional Theory, where we have utilized the Crank-Nicholson propagator. We found that in pristine carbon nanotubes, the emitted charge tends to emerge mostly from electrons that are concentrated at the nanotube tip region. The charge beam concentrates into specific channel structures, showing the utility of carbon nanotubes in precision emission applications.Comment: Submitted to Physica

Extracorporeal Shock Wave Therapy: Non‐Urological Indications and Recent Trends

Extracorporeal shockwave lithotripsy (ESWL) was introduced in 1980 as the preferred tool by the urologist for the treatment of renal stones and or upper ureteral stones. ESWL is minimally invasive procedures, exposes patients to fewer anesthesias, and has equivalent stone‐free rates comparable to open surgery and endourology interventions for the treatment of renal stones. Urolithiasis is not the only application for extracorporeal shock waves but there are also other applications for it. Extracorporeal shock wave is used for the treatment of gall bladder stones, common bile duct stone clearance, pancreatic calculi, salivary stones, erectile dysfunction, and refractory angina pectoris chronic wound healing. This chapter gives full review about ESWL as minimally invasive procedures in the following items: (i) ESWL l in treatment of gall stones; (ii) ESWL for common bile duct (CBD) stones; (iii) ESWL for pancreatic stones associated with pancreatic pseudo cysts and chronic pancreatitis; (iv) ESWL in the treatment of salivary stones; (v) ESWL in the treatment of erectile dysfunction (ED); (vi) Cardiac shock wave therapy (ESWL) in treatment of refractory angina (RA); (vii) ESWL and chronic wound healing; (viii) Recent trends in extracorporeal shockwave lithotripsy (ESWL); (ix) Post ESWL complementary therapy; and (x) The future of ESWL in the year 2038

The impact of increased body mass index on the outcomes after valve replacement

Background: The association between obesity and the outcomes of surgery is controversial. This study aimed to assess the effect of body mass index (BMI) on early and late morbidity and mortality after valve replacement surgery. Methods: The study was conducted on 100 valve replacement patients from 2020 to 2022. The patients were divided according to BMI into two groups: patients with morbid obesity (BMI ≥ 35) (Group A: n = 50) and patients who had BMI< 35 (Group B.; n = 50). Results: Hospital (9.43 ± 5.93 vs. 7.25 ± 4.05 days, P= 0.034), ICU length of stay (7.32 ± 5.67 vs. 4.52 ± 3.24 days, P= 0.003), and duration of mechanical ventilation (3.58 ± 2.54 vs. 2.342 ± 2.05 days, P= 0.008) were significantly higher in Group A. There was no significant difference in postoperative mortality between both groups (P= 0.678). There was no significant difference in morbidity and mortality after a 3-month follow-up. Hemoglobin was significantly lower in Group A (P =0.034), with no difference in postoperative laboratory investigations. Conclusions: Morbid obesity was not associated with increased mortality after valve replacement surgery; however, it could increase the duration of ventilation, ICU, and hospital stay

Assessment of Sexual Functions among Male Tramadol Users

Background: Opioids are effective for chronic pain but can have adverse effects on male sexual function. Objectives: This study aimed to evaluate sexual functions among male tramadol users. Patients and Methods: This case-control study involved 45 male tramadol users and 45 healthy controls. Psychometric tests, such as the International Index of Erectile Function (IIEF), the Sexual Quality of Life Questionnaire-Male Version (SQOL-M), and addiction severity index were given to the participants. They also had clinical interviews and hormonal tests (testosterone, LH, FSH, and prolactin). The primary outcome was erectile dysfunction prevalence. The secondary outcomes included the correlation between ED severity and abuse duration, dosing frequency, addiction severity index, and anxiety and depression scales. Results: Tramadol users had significantly lower total testosterone and LH levels, but higher prolactin compared to controls. Rates of erectile dysfunction were markedly higher in the tramadol group (57.8% vs. 13.3%, p < 0.001). Tramadol users scored significantly worse on the IIEF domains of erectile function, sexual desire, orgasmic function, satisfaction, and preoccupation. Tramadol users also significantly reduced their SQOL-M scores. The duration of use, dosing frequency, and addiction severity index positively correlate with the severity of erectile dysfunction. Conclusion: Extended use of tramadol has been associated with hormonal disruption, an increased prevalence of erectile dysfunction, impairments in various aspects of sexual function, and a reduced quality of sexual life in males

Cannabis-induced impairment of learning and memory

Cannabis sativa preparations are the most commonly used illicit drugs worldwide. The present study aimed to investigate the effect of Cannabis sativa extract in the working memory version of the Morris water maze (MWM; Morris, 1984) test and determine the effect of standard memory enhancing drugs. Cannabis sativa was given at doses of 5, 10 or 20 mg/kg (expressed as Δ^9-tetrahydrocannabinol) alone or co-administered with donepezil (1 mg/kg), piracetam (150 mg/ kg), vinpocetine (1.5 mg/kg) or ginkgo biloba (25 mg/kg) once daily subcutaneously (s.c.) for one month. Mice were examined three times weekly for their ability to locate a submerged platform. Mice were euthanized 30 days after starting cannabis injection when biochemical assays were carried out. Malondialdehyde (MDA), reduced glutathione (GSH), nitric oxide, glucose and brain monoamines were determined. Cannabis resulted in a significant increase in the time taken to locate the platform and enhanced the memory impairment produced by scopolamine. This effect of cannabis decreased by memory enhancing drugs with piracetam resulting in the most-shorter latency compared with the cannabis. Biochemically, cannabis altered the oxidative status of the brain with decreased MDA, increased GSH, but decreased nitric oxide and glucose. In cannabis-treated rats, the level of GSH in brain was increased after vinpocetine and donepezil and was markedly elevated after Ginkgo biloba. Piracetam restored the decrease in glucose and nitric oxide by cannabis. Cannabis caused dose-dependent increases of brain serotonin, noradrenaline and dopamine. After cannabis treatment, noradrenaline is restored to its normal value by donepezil, vinpocetine or Ginkgo biloba, but increased by piracetam. The level of dopamine was significantly reduced by piracetam, vinpocetine or Ginkgo biloba. These data indicate that cannabis administration is associated with impaired memory performance which is likely to involve decreased brain glucose availability as well as alterations in brain monoamine neurotransmitter levels. Piracetam is more effective in ameliorating the cognitive impairments than other nootropics by alleviating the alterations in glucose, nitric oxide and dopamine in brain

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic