p53 mutation biases squamocolumnar junction progenitor cells towards dysplasia rather than metaplasia in Barrett's oesophagus.

BACKGROUND: While p53 mutations occur early in Barrett's oesophagus (BE) progression to oesophageal adenocarcinoma (EAC), their role in gastric cardia stem cells remains unclear. OBJECTIVE: This study investigates the impact of p53 mutation on the fate and function of cardia progenitor cells in BE to EAC progression, particularly under the duress of chronic injury. DESIGN: We used a BE mouse model (L2-IL1ß) harbouring a Trp53 mutation (R172H) to study the effects of p53 on Cck2r+ cardia progenitor cells. We employed lineage tracing, pathological analysis, organoid cultures, single-cell RNA sequencing (scRNA-seq) and computational analyses to investigate changes in progenitor cell behaviour, differentiation patterns and tumour progression. Additionally, we performed orthotopic transplantation of sorted metaplastic and mutant progenitor cells to assess their tumourigenic potential in vivo. RESULTS: The p53 mutation acts as a switch to expand progenitor cells and inhibit their differentiation towards metaplasia, but only amidst chronic injury. In L2-IL1ß mice, p53 mutation increased progenitors expansion and lineage-tracing with a shift from metaplasia to dysplasia. scRNA-seq revealed dysplastic cells arise directly from mutant progenitors rather than progressing through metaplasia. In vitro, p53 mutation enhanced BE progenitors' organoid-forming efficiency, growth, DNA damage resistance and progression to aneuploidy. Sorted metaplastic cells grew poorly with no progression to dysplasia, while mutant progenitors gave rise to dysplasia in orthotopic transplantation. Computational analyses indicated that p53 mutation inhibited stem cell differentiation through Notch activation. CONCLUSIONS: p53 mutation contributes to BE progression by increasing expansion and fitness of undifferentiated cardia progenitors and preventing their differentiation towards metaplasia.

Hidden cases of epilepsy in cognitive impairment clinics: Exploring the use of a portable device for simplified electroencephalography testing.

Late-onset epilepsy, particularly focal impaired awareness seizures, often present without convulsions and can cause memory impairment. This can lead patients to initially seek consultation at memory clinics, potentially delaying referral to epilepsy specialists. We report on three patients, aged 40s to 70s, admitted for cognitive evaluation who were finally diagnosed with epileptic seizures as the underlying cause of their symptoms. Notably, all initially presented to local clinics with symptoms suggesting cognitive impairment. Despite initial diagnostic uncertainty, all patients exhibited epileptic activity on electroencephalography (EEG) and responded positively to antiepileptic drugs, suggesting epileptic mechanisms were involved in their symptoms. Both traditional clinical EEG systems and newly developed, one-minute portable EEG devices were used in their evaluations. The portable device, medically approved in Japan, successfully captured sharp-waves like activities with the same durations, amplitudes, and shapes as traditional devices. This highlights its potential to improve epilepsy diagnosis and future screening due to its portability and ease of use. Implementing portable EEG devices could promote timely and appropriate treatment, preventing misdiagnosis of neurological conditions.

A case with temporal spikes on electroencephalography induced by over 80 sessions of electroconvulsive therapy.

Background: Electroconvulsive therapy (ECT) is widely recognized as one of the most effective treatments for various psychiatric disorders and is generally considered safe. However, a few reports have mentioned that multiple ECT sessions could induce electroencephalography (EEG) abnormalities and epileptic seizures, a serious side effect of ECT. We experienced a case with EEG abnormalities after multiple ECT sessions and aimed to share our insights on conducting ECT safely. Case Presentation: We present the case of a 73-year-old female diagnosed with major depressive disorder. She underwent regular ECT sessions to alleviate her psychiatric symptoms. However, after more than 80 sessions, previously undetected EEG abnormalities were observed. Since the patient did not have clinical seizures, we were able to continue ECT at longer intervals without the use of antiepileptic drugs. Conclusion: Our case suggests the importance of routine EEG testing in patients undergoing prolonged ECT. While careful monitoring is necessary, continuing ECT without antiepileptic medication in patients with EEG abnormalities could be permissible.

A Case of Tension Subcutaneous Emphysema Treated With Minimally Invasive Open-Window Thoracostomy Using a Wound Protector/Retractor and Three-Sided Taping.

Subcutaneous emphysema is a common complication of thoracic surgery. Tension subcutaneous emphysema that causes airway obstruction is rare but life-threatening. This report presents a patient who developed tension subcutaneous emphysema after recurrent secondary pneumothorax surgery which was treated with minimally invasive open-window thoracostomy. A wound protector/retractor and three-sided taping were successfully used to prevent air from entering the subcutaneous space via the wound while draining trapped air without creating an open pneumothorax. This approach is an option for managing subcutaneous and intrathoracic air leakage in emergency situations.

Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases.

Background: The current biomarker-supported diagnosis of Alzheimer's disease (AD) is hindered by invasiveness and cost issues. This study aimed to address these challenges by utilizing portable electroencephalography (EEG). We propose a novel, non-invasive, and cost-effective method for identifying AD, using a sample of patients with biomarker-verified AD, to facilitate early and accessible disease screening. Methods: This study included 35 patients with biomarker-verified AD, confirmed via cerebrospinal fluid sampling, and 35 age- and sex-balanced healthy volunteers (HVs). All participants underwent portable EEG recordings, focusing on 2-minute resting-state EEG epochs with closed eyes state. EEG recordings were transformed into scalogram images, which were analyzed using "vision Transformer(ViT)," a cutting-edge deep learning model, to differentiate patients from HVs. Results: The application of ViT to the scalogram images derived from portable EEG data demonstrated a significant capability to distinguish between patients with biomarker-verified AD and HVs. The method achieved an accuracy of 73%, with an area under the receiver operating characteristic curve of 0.80, indicating robust performance in identifying AD pathology using neurophysiological measures. Conclusions: Our findings highlight the potential of portable EEG combined with advanced deep learning techniques as a transformative tool for screening of biomarker-verified AD. This study not only contributes to the neurophysiological understanding of AD but also opens new avenues for the development of accessible and non-invasive diagnostic methods. The proposed approach paves the way for future clinical applications, offering a promising solution to the limitations of advanced diagnostic practices for dementia.

Sustained Clinical Complete Response after Discontinuation of Trastuzumab-deruxetecan Due to Interstitial Pneumonia for HER2-positive Gastric adenocarcinoma with Enteroblastic Differentiation (GAED): A Case Report.

Trastuzumab deruxtecan (T-DXd) has demonstrated remarkable efficacy as a third- or later-line chemotherapy for human epidermal growth factor receptor 2 (HER2)-positive advanced gastric and gastroesophageal junction adenocarcinomas. However, it may cause pneumonitis, and its efficacy in rare histologies such as gastric adenocarcinoma with enteroblastic differentiation (GAED) remains unclear. A 74-year-old woman with unresectable HER2-positive GAED and lung metastasis received T-DXd as a fifth-line chemotherapy. Treatment was discontinued after 15 cycles owing to drug-induced pneumonitis; however, the patient achieved a sustained complete response for 14 months without subsequent chemotherapy or the exacerbation of pneumonitis. T-DXd was effective in HER2-positive GAED.

A Case of Tension Pneumomediastinum Treated With Mediastinal Drainage Using a Semi-flexible Fiberscope via a Subxiphoid Approach.

Tension pneumomediastinum with hemodynamic failure is a rare but life-threatening condition. Rapid decompression of the mediastinum by drainage is essential to save the patient's life. This report presents a case of tension pneumomediastinum that developed during conservative management of a pneumomediastinum associated with idiopathic pulmonary fibrosis. Endoscopically guided mediastinal drainage was successfully performed in the emergency situation of tension pneumomediastinum. Using the semi-flexible fiberscope inserted through a subxiphoid approach, the drainage catheter was easily and safely placed at the appropriate site in the mediastinum. Good mediastinal decompression was achieved, and the patient was out of this critical condition.

Impaired Glycosylation of Gastric Mucins Drives Gastric Tumorigenesis and Serves as a Novel Therapeutic Target.

BACKGROUND & AIMS: Gastric cancer is often accompanied by a loss of mucin 6 (MUC6), but its pathogenic role in gastric carcinogenesis remains unclear. METHODS: Muc6 knockout (Muc6-/-) mice and Muc6-dsRED mice were newly generated. Tff1Cre, Golph3-/-, R26-Golgi-mCherry, Hes1flox/flox, Cosmcflox/flox, and A4gnt-/- mice were also used. Histology, DNA and RNA, proteins, and sugar chains were analyzed by whole-exon DNA sequence, RNA sequence, immunohistochemistry, lectin-binding assays, and liquid chromatography-mass spectrometry analysis. Gastric organoids and cell lines were used for in vitro assays and xenograft experiments. RESULTS: Deletion of Muc6 in mice spontaneously causes pan-gastritis and invasive gastric cancers. Muc6-deficient tumor growth was dependent on mitogen-activated protein kinase activation, mediated by Golgi stress-induced up-regulation of Golgi phosphoprotein 3. Glycomic profiling revealed aberrant expression of mannose-rich N-linked glycans in gastric tumors, detected with banana lectin in association with lack of MUC6 expression. We identified a precursor of clusterin as a binding partner of mannose glycans. Mitogen-activated protein kinase activation, Golgi stress responses, and aberrant mannose expression are found in separate Cosmc- and A4gnt-deficient mouse models that lack normal O-glycosylation. Banana lectin-drug conjugates proved an effective treatment for mannose-rich murine and human gastric cancer. CONCLUSIONS: We propose that Golgi stress responses and aberrant glycans are important drivers of and promising new therapeutic targets for gastric cancer.

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.

Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations.

Introduction: Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice. Objective: We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device. Methods: We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery. Results: Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93. Discussion: This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients.

Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms.

Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of the 87 patients, 24 (27.6%) developed postoperative delirium. Age, use of psychotropic drugs, cognitive function (Mini-Cog < 4), index of activities of daily living (Barthel Index < 100), history of stroke or cerebral hemorrhage, and eGFR (estimated glomerular filtration rate) < 60 were selected to develop delirium prediction models. The Extra-trees model had the best area under the receiver operating characteristic curve (0.76 [standard deviation 0.11]; sensitivity: 0.63; specificity: 0.78). XGBoost showed the highest sensitivity (AUROC, 0.75 [0.07]; sensitivity: 0.67; specificity: 0.79). Machine learning algorithms could predict post-cardiovascular delirium using preoperative data.Trial registration: UMIN-CTR (ID; UMIN000049390).

Inter-brain synchrony during mother-infant interactive parenting in 3-4-month-old infants with and without an elevated likelihood of autism spectrum disorder.

Maternal bonding for mammalian infants is critical for their survival. Additionally, it is important for human infants' development into social creatures. However, despite the ample neurobiological evidence of attachment for the mother's brain, the interplay of this system in infants is poorly understood. We aimed to identify the neural substrates of synchrony in mothers and infants under three interactive conditions and compare the differences between groups with (n = 16) and without (n = 71) an elevated likelihood of autism spectrum disorder by examining the inter-brain synchrony between mothers and their 3-4-month-old infants. Mother-infant hyperscanning with functional near-infrared spectroscopy was performed during breastfeeding and while each of the mother and experimenter was holding the infants. The results showed almost no group differences, with both groups demonstrating the strongest inter-brain coupling for breastfeeding. The cerebral foci underlying these couplings differed between mothers and infants: the ventral prefrontal cortex, focusing on the right orbitofrontal cortex, in the mother and the left temporoparietal junction in the infant were chiefly involved in connecting the two brains. Furthermore, these synchronizations revealed many significant correlations with behavioral measures, including subsequent language development. The maternal reward-motivational system and the infant's elementary mentalization system seem to underlie mother-infant coupling during breastfeeding.

EEG resting-state networks in Alzheimer's disease associated with clinical symptoms.

Alzheimer's disease (AD) is a progressive neuropsychiatric disease affecting many elderly people and is characterized by progressive cognitive impairment of memory, visuospatial, and executive functions. As the elderly population is growing, the number of AD patients is increasing considerably. There is currently growing interest in determining AD's cognitive dysfunction markers. We used exact low-resolution-brain-electromagnetic-tomography independent-component-analysis (eLORETA-ICA) to assess activities of five electroencephalography resting-state-networks (EEG-RSNs) in 90 drug-free AD patients and 11 drug-free patients with mild-cognitive-impairment due to AD (ADMCI). Compared to 147 healthy subjects, the AD/ADMCI patients showed significantly decreased activities in the memory network and occipital alpha activity, where the age difference between the AD/ADMCI and healthy groups was corrected by linear regression analysis. Furthermore, the age-corrected EEG-RSN activities showed correlations with cognitive function test scores in AD/ADMCI. In particular, decreased memory network activity showed correlations with worse total cognitive scores for both Mini-Mental-State-Examination (MMSE) and Alzheimer's Disease-Assessment-Scale-cognitive-component-Japanese version (ADAS-J cog) including worse sub-scores for orientation, registration, repetition, word recognition and ideational praxis. Our results indicate that AD affects specific EEG-RSNs and deteriorated network activity causes symptoms. Overall, eLORETA-ICA is a useful, non-invasive tool for assessing EEG-functional-network activities and provides better understanding of the neurophysiological mechanisms underlying the disease.

Frontal midline theta rhythm and gamma activity measured by sheet-type wearable EEG device.

Introduction: The current study measured the frontal midline theta rhythm (Fmθ), which appears in the frontal midline region during the attentional focus state, using the sheet-type wearable electroencephalograph (EEG) device HARU-1, and examined the modulation of frontal gamma band activity by cognitive tasks. Methods: We measured the frontal EEG of 20 healthy subjects using HARU-1 for 2 min during the rest eyes-closed condition and simple mental calculation task condition, respectively. Statistical analyses were conducted using permutation testing based on t-test and cluster analysis to compare the results between the resting state and the task condition. Results: Twelve of 20 subjects showed Fmθ during the task condition. The 12 subjects with Fmθ showed significantly higher activity of the theta and gamma bands, and significantly low activity of the alpha band during the task condition compared to the resting condition. In the eight subjects without Fmθ were significantly low activity of the alpha and beta bands and no significant activity in the theta and gamma band activity during the task condition compared to the resting condition. Discussion: These results indicate that it is possible to measure Fmθ using HARU-1. A novel finding was the gamma band activity appearing with Fmθ in the left and right frontal forehead regions, suggesting that it reflects the function of the prefrontal cortex in working memory tasks.

Precise Discrimination for Multiple Etiologies of Dementia Cases Based on Deep Learning with Electroencephalography.

INTRODUCTION: It is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia problems under world-wide rapid increasing of patients with dementia. In this sense, electroencephalography (EEG) has been utilized as a promising examination to screen and assist in diagnosing dementia, with advantages of sensitiveness to neural functions, inexpensiveness, and high availability. Moreover, the algorithm-based deep learning can expand EEG applicability, yielding accurate and automatic classification easily applied even in general hospitals without any research specialist. METHODS: We utilized a novel deep neural network, with which high accuracy of discrimination was archived in neurological disorders in the previous study. Based on this network, we analyzed EEG data of healthy volunteers (HVs, N = 55), patients with Alzheimer's disease (AD, N = 101), dementia with Lewy bodies (DLB, N = 75), and idiopathic normal pressure hydrocephalus (iNPH, N = 60) to evaluate the discriminative accuracy of these diseases. RESULTS: High discriminative accuracies were archived between HV and patients with dementia, yielding 81.7% (vs. AD), 93.9% (vs. DLB), 93.1% (vs. iNPH), and 87.7% (vs. AD, DLB, and iNPH). CONCLUSION: This study revealed that the EEG data of patients with dementia were successfully discriminated from HVs based on a novel deep learning algorithm, which could be useful for automatic screening and assisting diagnosis of dementia diseases.

Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis.

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.

Non-Helicobacter pylori Gastric Microbiome Modulates Prooncogenic Responses and Is Associated With Gastric Cancer Risk.

Background and Aims: Although Helicobacter pylori is the most important bacterial carcinogen in gastric cancer (GC), GC can emerge even after H. pylori eradication. Studies suggest that various constituents of the gastric microbiome may influence GC development, but the role of individual pathogens is unclear. Methods: Human gastric mucosal samples were analyzed by 16SrRNA sequencing to investigate microbiome composition and its association with clinical parameters, including GC risk. Identified bacteria in the stomach were cocultured with gastric epithelial cells or inoculated into mice, and transcriptomic changes, DNA damage, and inflammation were analyzed. Bacterial reads in GC tissues were examined together with transcriptomic and genetic sequencing data in the cancer genome atlas dataset. Results: Patients after Helicobacter pylori eradication formed 3 subgroups based on the microbial composition revealed by 16SrRNA sequencing. One dysbiotic group enriched with Fusobacterium and Neisseria species was associated with a significantly higher GC incidence. These species activated prooncogenic pathways in gastric epithelial cells and promoted inflammation in mouse stomachs. Sugar chains that constitute gastric mucin attenuate host-bacteria interactions. Metabolites from Fusobacterium species were genotoxic, and the presence of the bacteria was associated with an inflammatory signature and a higher tumor mutation burden. Conclusion: Gastric microbiota in the dysbiotic stomach is associated with GC development after H. pylori eradication and plays a pathogenic role through direct host-bacteria interaction.

Evaluation of a Telehealth Parent-Training Program in Japan: Collaboration with Parents to Teach Novel Mand Skills to Children Diagnosed with Autism Spectrum Disorder.

This study developed a telehealth parent-training program to teach parents of children with autism spectrum disorder the process of mand-training implementation in Japan, and to further the international dissemination of evidence-based training strategies. Parent-training sessions were based on a behavioral skills training (BST) model, combined with weekly graphic and video feedback. The sessions were conducted by a board-certified behavior analyst-doctoral residing in Japan. Four parents with children with autism spectrum disorder participated in this study. The results preliminarily support the effectiveness and social validity of the program. This study extends previous parent-training research conducted in Japan by comprising all of the following features: (1) online program design; (2) mand training; (3) BST model; (4) session-by-session data on children's behavioral changes and procedural integrity; (5) within-subject experimental design; and (6) social validity evaluation.