Mitsubishi Corporation (Japan)

Post-COVID-19 Vaccination Syndrome (PCVS) refers to persistent, multisystemic symptoms developing after SARS-CoV-2 immunization. We established a nationwide registry across 14 Japanese outpatient clinics and analyzed 179 “clinically definitive” cases from 279 enrollees (December 2020–August 2023). All adverse events (AEs) were coded using MedDRA/J Preferred Terms and System Organ Classes; severity followed NCI-CTCAE V5.0. The cohort (66.5% female; mean age 59 years) experienced 493 AEs (median 2 per patient, range 1–29). Three System Organ Classes—General Disorders (29.2%), Nervous System Disorders (22.3%), and Musculoskeletal Disorders (10.1%)—accounted for 61.7% of events; fatigue, brain fog, dizziness, and extremity pain predominated. While 69.4% of AEs arose within 90 days post-vaccination, 12.4% appeared ≥ 360 days later. Severe AEs (≥ Grade 3) occurred in 14.6% of patients; overall improvement was 65.1%, leaving 29.4% unresolved. A provisional phenotype classification combining symptom patterns, onset timing, and severity identified high-risk subgroups with > 60% non-recovery rates. These findings underscore the need for enhanced post-vaccination surveillance systems and comprehensive care frameworks specifically tailored to address the protean manifestations and persistent nature of PCVS.

Student knowledge profile is the basis for adaptive learning applications in online learning resulting from modeling the student mastery of knowledge concepts. In recent years, typical works based on knowledge tracing (KT) expect to profile students and have achieved significant success for the next performance prediction. However, in practical online learning scenarios, current methods tend to suffer from the following challenges: 1) Prediction inconsistency: The accuracy of the next performance prediction is inconsistent with the accuracy of student knowledge profile prediction, which is the more required result. 2) Cold start of knowledge: In online learning scenarios, it is often necessary to profile some knowledge concepts without learning records in advance. In this paper, we propose a novel Graph-based Student Knowledge Profile Model (GSKPM), along with a new end-to-end training objective, to tackle these challenges. We first define a new training objective to ensure the model is capable of inferring consistent student knowledge profiles. Then in this model, a two-stage hyper-aggregation process is employed to make full use of the topological relations between knowledge concepts and knowledge domains to provide information during profiling, especially for cold start knowledge concepts. Finally, through extensive experiments on real-world datasets, we will show that GSKPM achieves better prediction performances on student knowledge profiles and well deals with the cold start problem.

Nicotinamide mononucleotide (NNM) is an orally bioavailable NAD+ precursor that has demonstrated beneficial effects against aging and aging-associated diseases in animal models. NMN is ultimately converted to NAD+, a redox cofactor that mediates many metabolic enzymes. NAD+ also serves as the substrate for poly(ADP-ribose) polymerase (PARP) and sirtuins, and regulates various biological processes, such as metabolism, DNA repair, gene expression, and stress responses. Previous mouse models showed that NMN administration can increase NAD+ in various organs and ameliorate aging-related diseases, such as obesity, diabetes, heart failure, stroke, kidney failure, and Alzheimer’s disease through NAD+-mediated pathways. However, evidence of its effect on humans is still scarce. In this study, we conducted a placebo-controlled, randomized, double blind, parallel-group trial to investigate the safety of orally administered NMN and its efficacy to increase NAD+ levels in thirty healthy subjects. Healthy volunteers received 250 mg/day of NMN (n = 15) or placebo (n = 15) for 12 weeks, and physiological and laboratory tests were performed during this period. In addition, NAD+ and its related metabolites in whole blood were examined. Oral supplementation of NMN for 12 weeks caused no abnormalities in physiological and laboratory tests, and no obvious adverse effects were observed. NAD+ levels in whole blood were significantly increased after NMN administration. We also observed the significant rise in nicotinic acid mononucleotide (NAMN) levels, but not in NMN. We also found that the increased amount of NAD+ was strongly correlated with pulse rate before the administration of NMN. These results suggest that oral administration of NMN is a safe and practical strategy to boost NAD+ levels in humans.Clinical Trial Registration: JRCT [https://jrct.niph.go.jp/], identifier: [jRCTs041200034].

Deteriorating sleep quality and physical or mental fatigue in older adults leads to decreased quality of life and increased mortality rates. This study investigated the effects of the time-dependent intake of nicotinamide mononucleotide (NMN) on sleep quality, fatigue, and physical performance in older adults. This randomized, double-blind placebo-controlled study evaluated 108 participants divided into four groups (NMN_AM; antemeridian, NMN_PM; post meridian, Placebo_AM, Placebo_PM). NMN (250 mg) or placebo was administered once a day for 12 weeks. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index. Fatigue was evaluated using the “Jikaku-sho shirabe” questionnaire. Grip strength, 5-times sit-to-stand (5-STS), timed up and go, and 5-m habitual walk were evaluated to assess the physical performance. Significant interactions were observed between 5-STS and drowsiness. 5-STS of all groups on post-intervention and drowsiness of the NMN_PM and Placebo_PM groups on mid- and post-intervention showed significant improvement compared with those in pre-intervention. The NMN_PM group demonstrated the largest effect size for 5-STS (d = 0.72) and drowsiness (d = 0.64). Overall, NMN intake in the afternoon effectively improved lower limb function and reduced drowsiness in older adults. These findings suggest the potential of NMN in preventing loss of physical performance and improving fatigue in older adults.

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment.