The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

BACKGROUND/AIMS: Cerulein pancreatitis is similar to human edematous pancreatitis with dysregulation of the production and secretion of digestive enzymes, edema formation, cytoplasmic vacuolization and the death of acinar cells. We hypothesized that membrane proteins may be altered as the early event during the induction of acute pancreatitis. Present study aims to determine the differentially expressed proteins in the membranes of cerulein-treated pancreatic acinar cells. METHODS: Pancreatic acinar AR42J cells were treated with 10(-8) M cerulein for 1 hour. Membrane proteins were isolated from the cells and separated by two-dimensional electrophoresis using pH gradients of 5-8. Membrane proteins were identified by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis of the peptide digests. The differentially expressed proteins, whose expression levels were more or less than three-fold in cerulein-treated cells, were analyzed. RESULTS: Two differentially expressed proteins (mannan-binding lectin-associated serine protease-2, heat shock protein 60) were up-regulated while four proteins (protein disulfide isomerase, gamma-actin, isocitrate dehydrogenase 3, seven in absentia homolog 1A) were down-regulated by cerulein treatment in pancreatic acinar cells. These proteins are related to cell signaling, oxidative stress, and cytoskeleton arrangement. CONCLUSIONS: Oxidative stress may induce cerulein-induced cell injury and disturbances in defense mechanism in pancreatic acinar cells.
Acinar Cells ; Actins ; Caerulein ; Cytoplasm ; Cytoskeleton ; Edema ; Electrophoresis ; Heat-Shock Proteins ; Humans ; Isocitrate Dehydrogenase ; Isocitrates ; Mass Spectrometry ; Membrane Proteins ; Membranes ; Oxidative Stress ; Pancreatitis ; Protein Disulfide-Isomerases ; Proteins ; Proteome ; Proton-Motive Force ; Serine

BACKGROUND: To prevent medically significant errors, hospital laboratories must perform thorough statistical quality control (QC) procedures. We surveyed the QC status of small laboratories and created new statistical QC software that they can easily use for improving QC. METHODS: A questionnaire on the status of external and internal QC was created and sent to clinics and hospitals with small-scale laboratories. We then created QC software that can be downloaded and installed for free. RESULTS: External quality assessments were performed in 32% of the clinics (22 of 66) and 52% of the hospitals (12 of 23). Seventy-three percent of all institutions (66 of 90) carried out an internal quality assessment based on their own guidelines, mostly using commercialized QC materials. However, only 52% of clinics and 23% of hospitals used their own acceptable range instead of the manufacturer's expected range. In addition, the proportion of manual QC management reached 52% in clinics and 82% in hospitals. The QC software we designed covers all the basic functions of statistical QC and aims to improve the quality of laboratories. CONCLUSIONS: We obtained basic data on the current status of external and internal QC in small-scale laboratories using this survey. Furthermore, we suggested that new QC software may actually help to improve QC of small laboratories.
Laboratories, Hospital* ; Quality Assurance, Health Care ; Quality Control*

BACKGROUND/AIMS: To determine the changes in body weight and bone mineral density in patients with ankylosing spondylitis (AS) receiving anti-tumor necrosis factor-alpha (TNF-alpha) treatment. METHODS: Thirty-one patients with AS (25 males and 6 females) who fulfilled the Modified New York Criteria for AS were included in this retrospective study. All patients had active disease that eventually required anti-TNF-alpha treatment. Each patient received anti-TNF-alpha treatment (etanercept 25 mg twice weekly or adalimumab 40 mg twice monthly) for more than 2 years. Body weight, disease activity as Bath ankylosing spondylitis disease activity index (BASDAI), C-reactive protein, erythrocyte sedimentation rate (ESR), lumbar bone mineral density (LBMD), and femoral bone mineral density (FBMD) were measured at baseline and at 1 and 2 years after initiating anti-TNF-alpha treatment. RESULTS: There was a significant increase in mean body weight at 1 year (1.1 +/- 3.8 kg) and at 2 years (1.7 +/- 4.8 kg) compared with baseline. The gains in mean BMD of the lumbar spine were significant at 1 year (0.4 +/- 0.4) and 2 years (0.5 +/- 0.7) compared with baseline. Mean BMD of the femur was also increased at 1 year (0.08 +/- 0.7) and 2 years (0.1 +/- 0.8) compared with baseline, but these differences were not statistically significant. There were significant decreases in BASDAI at 1 year (-3.3 +/- 2.8) and at 2 years (-3.6 +/- 2.8) compared with baseline. CONCLUSIONS: This study showed significant increases in body weight, lumbar BMD, and BASDAI at 1 year and 2 years in patients with ankylosing spondylitis after receiving anti-TNF-alpha treatment.
Antibodies, Monoclonal, Humanized ; Baths ; Blood Sedimentation ; Body Weight* ; Bone Density* ; C-Reactive Protein ; Cachexia ; Femur ; Humans ; Male ; Necrosis* ; Retrospective Studies ; Spine ; Spondylitis ; Spondylitis, Ankylosing* ; Adalimumab

BACKGROUND/AIMS: To determine the changes in body weight and bone mineral density in patients with ankylosing spondylitis (AS) receiving anti-tumor necrosis factor-alpha (TNF-alpha) treatment. METHODS: Thirty-one patients with AS (25 males and 6 females) who fulfilled the Modified New York Criteria for AS were included in this retrospective study. All patients had active disease that eventually required anti-TNF-alpha treatment. Each patient received anti-TNF-alpha treatment (etanercept 25 mg twice weekly or adalimumab 40 mg twice monthly) for more than 2 years. Body weight, disease activity as Bath ankylosing spondylitis disease activity index (BASDAI), C-reactive protein, erythrocyte sedimentation rate (ESR), lumbar bone mineral density (LBMD), and femoral bone mineral density (FBMD) were measured at baseline and at 1 and 2 years after initiating anti-TNF-alpha treatment. RESULTS: There was a significant increase in mean body weight at 1 year (1.1 +/- 3.8 kg) and at 2 years (1.7 +/- 4.8 kg) compared with baseline. The gains in mean BMD of the lumbar spine were significant at 1 year (0.4 +/- 0.4) and 2 years (0.5 +/- 0.7) compared with baseline. Mean BMD of the femur was also increased at 1 year (0.08 +/- 0.7) and 2 years (0.1 +/- 0.8) compared with baseline, but these differences were not statistically significant. There were significant decreases in BASDAI at 1 year (-3.3 +/- 2.8) and at 2 years (-3.6 +/- 2.8) compared with baseline. CONCLUSIONS: This study showed significant increases in body weight, lumbar BMD, and BASDAI at 1 year and 2 years in patients with ankylosing spondylitis after receiving anti-TNF-alpha treatment.
Antibodies, Monoclonal, Humanized ; Baths ; Blood Sedimentation ; Body Weight* ; Bone Density* ; C-Reactive Protein ; Cachexia ; Femur ; Humans ; Male ; Necrosis* ; Retrospective Studies ; Spine ; Spondylitis ; Spondylitis, Ankylosing* ; Adalimumab

Spontaneous regression of tumors is an extremely rare event in hepatocellular carcinoma (HCC) with only a few reports available. With the accumulation of clinical information and tumor immunogenetics, several mechanisms for the cystic changes of HCC have been suggested, including arterial thrombosis, inflammation, and rapid tumor growth. This paper reports an uncommon case of the partial regression of HCC, which was initially misdiagnosed as a mucinous cystic neoplasm of the liver due to the unusual radiologic findings. A 78-year-old female with the hepatitis B virus and liver cirrhosis presented with an approximately 5 cm-sized cystic mass of the liver. From the radiologic evidence of a papillary-like projection from the cyst wall toward the inner side, the initial impression was a mucinous cystic neoplasm of the liver. The patient underwent a surgical resection and finally, cystic degeneration of HCC, in which approximately 80% necrosis was noted. This case suggests that if a cystic neoplasm of liver appears in a patient with a high risk of HCC on a hepatobiliary imaging study, it is prudent to consider the cystic degeneration of HCC in a differential diagnosis.
Aged ; Carcinoma, Hepatocellular ; Diagnosis, Differential ; Diagnostic Errors ; Female ; Hepatitis B virus ; Humans ; Immunogenetics ; Inflammation ; Liver ; Liver Cirrhosis ; Liver Neoplasms ; Magnetic Resonance Imaging ; Mucins ; Necrosis ; Thrombosis

Spontaneous regression of tumors is an extremely rare event in hepatocellular carcinoma (HCC) with only a few reports available. With the accumulation of clinical information and tumor immunogenetics, several mechanisms for the cystic changes of HCC have been suggested, including arterial thrombosis, inflammation, and rapid tumor growth. This paper reports an uncommon case of the partial regression of HCC, which was initially misdiagnosed as a mucinous cystic neoplasm of the liver due to the unusual radiologic findings. A 78-year-old female with the hepatitis B virus and liver cirrhosis presented with an approximately 5 cm-sized cystic mass of the liver. From the radiologic evidence of a papillary-like projection from the cyst wall toward the inner side, the initial impression was a mucinous cystic neoplasm of the liver. The patient underwent a surgical resection and finally, cystic degeneration of HCC, in which approximately 80% necrosis was noted. This case suggests that if a cystic neoplasm of liver appears in a patient with a high risk of HCC on a hepatobiliary imaging study, it is prudent to consider the cystic degeneration of HCC in a differential diagnosis.
Aged ; Carcinoma, Hepatocellular ; Diagnosis, Differential ; Diagnostic Errors ; Female ; Hepatitis B virus ; Humans ; Immunogenetics ; Inflammation ; Liver ; Liver Cirrhosis ; Liver Neoplasms ; Magnetic Resonance Imaging ; Mucins ; Necrosis ; Thrombosis