Alcohol consumption is responsible for approximately 6% of all deaths and 5.1% of the global disease burden. The most common alcohol-related causes of death include liver cirrhosis (50% of cases), pancreatitis (25%), and esophageal cancer (22%). In this review, we provide an overview of ethanol metabolism and highlight the major diseases caused by alcohol consumption in the liver and gastrointestinal tract. Due to its central metabolic role, the liver is particularly susceptible to ethanol, which is known to cause a wide spectrum of conditions, including steatosis, steatohepatitis, alcohol-associated hepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma). The gastrointestinal tract is often one of the first areas to show signs of damage from excessive alcohol consumption. Chronic alcohol abuse is a well-established risk factor for both acute and chronic pancreatitis, as well as pancreatic cancer. Approximately 70% of acute pancreatitis cases and 30% of chronic pancreatitis cases are attributable to alcohol abuse. Epidemiological studies have consistently demonstrated a positive correlation between alcohol intake and the prevalence of gallstones. Moreover, alcohol is an important risk factor for gastroenteropancreatic cancer, as ethanol metabolism produces acetaldehyde, a potent carcinogen for humans. In conclusion, chronic ethanol intake, through one of its main metabolic products, acetaldehyde, causes pathological changes in the gastrointestinal tract, liver, pancreas, and gallbladder. Even moderate amounts of alcohol may increase the risk of cancers, such as colorectal cancer. Therefore, if there is clinical suspicion of excessive alcohol intake in a patient with persistent digestive symptoms (e.g., abdominal pain, nausea, vomiting, diarrhea, and bloody stools), immediate medical evaluation is essential. Referral to specialized centers with expertise in alcohol use disorder is a key management option for patients with established alcohol use disorder.

Over the course of the COVID-19 pandemic and its aftermath, growing concerns have emerged about the mental health of children and youth. Disease, loss, and lockdowns presented young people with enormous stressors, and much research suggests elevated levels of pediatric depression, anxiety, suicidality, and obsessive-compulsive behavior. However, considerable debate remains about the nature and persistence of these symptoms. This narrative review, conducted approximately four years after the onset of the pandemic, summarizes the major findings from four years of research, including empirical studies, meta-analyses, and systematic reviews. Studies were sourced from scholarly databases using the keywords “COVID-19”, “children”, “adolescents”, and “mental health”. The existing literature on the prevalence of depression in youth indicated that worldwide rates varied from 2.2% to 11.8% of the population, with one study revealing that one in four young people reported depressive symptoms. More specifically, 44% of youth in the United States demonstrated depression, while in China, the prevalence rate ranged from 11% to 44% of young people. Reviewed data showed that 20% of youth globally endorsed symptoms of anxiety or stress reactions, with countries such as Denmark (44%), Canada (45%), and the United States (32%) reporting extremely high rates. In the implications section, recommendations for screening and intervention procedures are outlined.

High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

With the increasing use of artificial intelligence (AI) in diagnostics, AI algorithms have shown great potential in aiding diagnostics. As more of these algorithms are developed, there is overwhelming enthusiasm for implementing digital and artificial intelligence-based pathology (DAIP), but doubts and pitfalls are also emerging. However, few original or review articles address the limitations and practical aspects of implementing DAIP. In this review, we briefly examine the evidence related to the benefits and pitfalls of DAIP implementation and argue that DAIP is not suitable for every clinical laboratory.

We searched the PubMed database using the following keywords: “digital pathology,” “digital AI pathology,” and “AI pathology.”. Additionally, we incorporated personal experiences and manually searched related papers.

Ninety-two publications were found, of which 24 met the inclusion criteria. Many advantages of DAIP were discussed, including improved diagnostic accuracy and equity. However, several limitations of implementing DAIP exist, such as financial constraints, technical challenges, and legal/ethical concerns.

We found a generally favorable but cautious outlook for the implementation of DAIP in the pathology workflow. Many studies have reported promising outcomes in using AI for diagnosis and analysis; however, there are also several noteworthy limitations in implementing DAIP. Therefore, a balance between the benefits and pitfalls of DAIP must be thoroughly articulated and examined in light of the institution’s needs and goals before making the decision to implement DAIP. Approaches for mitigating machine learning biases were also proposed, and the adaptation and growth of the pathology profession were discussed in light of DAIP development and advances.

The quantitative effects of alcohol consumption on cirrhosis and hepatocellular carcinoma (HCC) in hepatitis B virus (HBV) infection are unknown. This study aimed to establish a dose-dependent model of alcohol consumption on the risks of cirrhosis and HCC.

PubMed, Embase, the Cochrane Library, Web of Science, and four Chinese databases were searched for studies published from their inception to 15 May 2024. A random-effects model was used to pool the data on the incidence of cirrhosis and HCC, and a dose-dependent model of alcohol’s effect on cirrhosis and HCC was established.

A total of 33,272 HBV patients from 45 studies were included. Compared with non-drinkers, the overall pooled odds ratio (OR) for cirrhosis was 2.61 (95% confidence interval [CI]: 1.46–4.66; I2 = 94%, p < 0.001), and the OR for HCC was 2.27 (95% CI: 1.50–3.43; I2 = 90%, p < 0.001) among drinkers. Compared with low-level drinkers, the estimated pooled OR for cirrhosis was 2.34 (95% CI: 1.59–3.44; I2 = 87%, p < 0.001), and the OR for HCC was 2.42 (95% CI: 1.90–3.09; I2 = 80%, p < 0.001) among high-level drinkers. Furthermore, a linear dose-dependent analysis showed that each daily consumption of 12 g of alcohol increased the risk of cirrhosis by 6.2% and the risk of HCC by 11.5%.

Alcohol dose-dependently increases the risks of cirrhosis and HCC in patients with HBV infection, and patients with daily alcohol consumption of more than 12 g should be strictly monitored.

Liver injury in Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) is a multifaceted disorder, lacking cohort homogeneity due to a variety of potential causes, including drugs, arsenic and other heavy metals, glyphosate, infections, and ultraviolet radiation. The goals of this review were (1) to analyze the role of diagnostic algorithms in assessing causality for potential culprits involved in the development of liver injury associated with immune-mediated SJS and TEN, which represent immune-based variant disorders within a continuous spectrum. Milder forms are classified as SJS or SJS/TEN overlap, while TEN is known as the most serious form; and (2) to interpret the findings that allow for the characterization of the different types of these disorders. The manuscript is based on an extensive literature search for single case reports, case cohorts, and review articles. Search terms included: Stevens-Johnson Syndrome, Toxic Epidermal Necrolysis, and specific diagnostic algorithms such as the Roussel Uclaf Causality Assessment Method (RUCAM) and the Algorithm of Drug Causality for Epidermal Necrolysis (ALDEN). For the purpose of basic feature description, the uniform term SJS/TEN is used in the current analysis. SJS/TEN presents with five different cohort types: SJS/TEN type (1), which refers to a cohort of SJS/TEN caused by drugs, as assessed by both ALDEN and RUCAM; type (2), representing SJS/TEN due to drugs and assessed by ALDEN only, but not by RUCAM; type (3), which includes a cohort of SJS/TEN caused by drugs, assessed by non-ALDEN and non-RUCAM tools; type (4), which focuses on a cohort of SJS/TEN caused by non-drug culprits, assessed by various tools; and type (5), which considers a cohort of SJS/TEN caused by unknown culprits. Using this new SJS/TEN typology will help better characterize individual features, personalize treatment, and clarify pathogenetic specifics for each of the five disease types. This new SJS/TEN typology provides clarity by replacing issues of inhomogeneity with cohort homogeneity.

The prevalence and fatality rates of cutaneous malignant melanoma (CMM) have been rising, particularly among the elderly. This study analyzes CMM incidence trends in the United States elderly population from 1987 to 2016 to inform prevention and management strategies.

Using incidence data from the Surveillance, Epidemiology, and End Results database spanning 1989 to 2008, we calculated the age-adjusted standardized population incidence rates for CMM in elderly individuals. The Joinpoint software was employed to estimate annual percent change and analyze trends in CMM incidence among elderly individuals from 1987 to 2016.

The study included 56,997 elderly CMM patients from eight Surveillance, Epidemiology, and End Results registries, of whom 36,726 were male (64.4%). The age-adjusted CMM incidence rate from 2012 to 2016 was 0.99 per 1,000, a 2.8-fold increase from 1987–1991 (95% confidence interval: 2.7–2.9). Incidence rates increased with age and birth cohort, peaking at 1.53 per 1,000 males and 0.59 per 1,000 females aged 85+ during 2012–2016. Birth cohort effects also showed a continuous increase.

This study reveals a substantial increase in CMM incidence rates among the elderly from 1987 to 2016, particularly between 2012 and 2016. Incidence rates escalated with age and birth cohort, with the highest rates observed in individuals aged 85 and older.

A mental disorder, also referred to as a psychiatric disorder or mental illness, is characterized by significant disturbances in an individual’s thinking, emotions, or behavior. In Ayurveda, herbal plants are used as alternative therapies for various ailments, including mental disorders. This review aims to provide a comprehensive overview of herbal medicines used in treating mental disorders in Sri Lanka. It relies on foundational books as primary sources to systematically identify and analyze the therapeutic potential of 24 traditional medicinal plants for treating mental disorders. Each plant was evaluated based on its scientific name, plant parts used, distribution in Sri Lanka, mechanisms of action, and identified phytochemicals. Furthermore, additional research was conducted using keywords such as mental disorders, herbal plants, plant distribution, phytochemicals, side effects, and mechanism of action through scientific databases. The phytochemicals present in these herbal plants possess antioxidant, anti-inflammatory, and neuroprotective properties, contributing to their potential antipsychotic activities. Trigonelline (from Abrus precatorius), bacosides (from Bacopa monnieri), asiaticoside and asiatic acid (from Centella asiatica), quercetin (from Ginkgo biloba), alliin and allicin (from Allium sativum), luteolin-7-O-glucoside (from Eclipta alba), and shogaol (from Zingiber officinale) demonstrate significant potential in modulating neurotransmitter levels, reducing oxidative stress, and alleviating symptoms associated with mental disorders such as depression, anxiety, and neurodegenerative diseases. The suggested therapeutic value of these identified herbal plants and their bioactive phytochemicals indicates the need for preserving and extensively investigating these remedies to establish their clinical effectiveness.

Early detection of hepatocellular carcinoma (HCC) is crucial for improving survival in patients with chronic hepatitis. The GALAD algorithm combines gender (biological sex), age, α-fetoprotein (AFP), Lens culinaris agglutinin-reactive fraction of AFP (AFP-L3), and protein induced by vitamin K absence or antagonist-II (PIVKA-II) for HCC detection. Similarly, the GAAD algorithm incorporates gender (biological sex), age, AFP, and PIVKA-II. This study aimed to assess the clinical utility of AFP-L3 in the GALAD algorithm and its potential synergies with ultrasound. We compared the clinical performance of GALAD with GAAD; AFP; AFP-L3; and PIVKA-II, with or without ultrasound, in Taiwanese adults.

A total of 439 serum samples were analyzed using a Cobas® e 601 analyzer (healthy controls, n = 200; chronic liver disease controls, n = 177; HCC cases, n = 62). Performance was assessed through receiver operating characteristic curve analyses to calculate the area under the curve.

The area under the curve for differentiating early-stage HCC from patients with chronic liver disease was optimal for PIVKA-II (84.9%), GAAD (79.8%), and GALAD (79.4%), with slightly improved performance for detecting all-stage HCC. Clinical performance was unaffected by disease stage or etiology. Sensitivity for early-stage HCC was highest for GAAD (57.6%) and GALAD (57.6%). Sensitivity for each strategy was further enhanced when combined with ultrasound, regardless of disease stage or etiology (P < 0.01).

These findings indicate that the role of AFP-L3 in the GALAD algorithm is minimal, supporting the use of GAAD for HCC detection. A combination of GAAD, GALAD, or PIVKA-II with ultrasound may improve diagnostic efficiency compared with recommended strategies.