No abstract

Background: Recent reports have indicated that loss-of-function mutations in the immunoglobulin superfamily member 1 gene (IGSF1, OMIM 300888) cause congenital central hypothyroidism with macroorchidism. Methods: We conducted a next-generation sequencing-based comprehensive mutation screening for pituitary hormone deficiencies to elucidate molecular mechanisms other than anatomical abnormalities of the pituitary that might be responsible for multiple anterior hormone deficiency in a male patient who originally visited our institute complaining of short stature. He was born large for gestational age (4,370 g, +3.0 SD) after an obstructed labour. Endocrinological evaluation revealed growth hormone and thyroid-stimulating hormone deficiency. Magnetic resonance imaging showed a discontinuity of the pituitary stalk with an ectopic posterior lobe and a hypoplastic anterior lobe, likely explaining multiple anterior pituitary hormone deficiency. Result: We identified a novel hemizygous IGSF1 mutation (c.1137_1138delCA, p.Asn380Glnfs*6) in the patient. In reviewing the literature, we noticed that all reported Japanese male IGSF1 mutation carriers were born larger than mean standards for gestational age (mean birth weight SD score of +2.0, 95% confidence interval 1.0-3.0). Conclusion: This case suggests that more attention should be paid to intrauterine growth and birth history when patients are suspected of having an IGSF1 mutation.

Glycogen storage disease type 1b (GSD-1b) is due to an autosomal recessive inborn error of carbohydrate metabolism caused by defects in glucose-6-phosphatase translocase. Patients with GSD-1b have severe hypoglycemia with several clinical manifestations of hepatomegaly, obesity, a doll-like face, and neutropenia. Liver transplantation has been indicated for severe glucose intolerance. This study retrospectively reviewed 4 children with a diagnosis of GSD-1b who underwent living-donor liver transplantation (LDLT). Between November 2005 and June 2008, 96 children underwent LDLT with overall patient and graft survival of 92.3%. Of these, 4 (4.2%) were indicated for GSD-1b. All patients are doing well with an excellent quality of life because of the stabilization of glucose intolerance, decreased hospital admission, and normalized neutrophil count. LDLT appears to be a feasible option and is associated with a better quality of life for patients with GSD-1b. Long-term observation may be necessary to collect sufficient data to confirm the efficacy of this treatment modality.

Recently, an international randomized controlled clinical trial showed that patients with SARS-CoV-2 infection treated orally with the 3-chymotrypsin-like protease (3CLpro) inhibitor PF-07321332 within three days of symptom onset showed an 89% lower risk of COVID-19-related hospital admission/ death from any cause as compared with the patients who received placebo. Lending support to this critically important result of the aforementioned trial, we demonstrated in our study that patients infected with a SARS-Cov-2 sub-lineage (B.1.1.284) carrying the Pro108Ser mutation in 3CLpro tended to have a comparatively milder clinical course (i.e., a smaller proportion of patients required oxygen supplementation during the clinical course) than patients infected with the same sub-lineage of virus not carrying the mutation. Characterization of the mutant 3CLpro revealed that the Kcat/Km of the 3CLpro enzyme containing Ser108 was 58% lower than that of Pro108 3CLpro. Hydrogen/deuterium-exchange mass spectrometry (HDX-MS) revealed that the reduced activity was associated with structural perturbation surrounding the substrate-binding region of the enzyme, which is positioned behind and distant from the 108th amino acid residue. Our findings of the attenuated clinical course of COVID-19 in patients infected with SARS-CoV-2 strains with reduced 3CLpro enzymatic activity greatly endorses the promising result of the aforementioned clinical trial of the 3CLpro inhibitor.

COVID-19 caused by SARS-CoV-2 is a worldwide problem. From the standpoint of hospital infection control, determining the source of infection is critical. We conducted the present study to evaluate the efficacy of using whole genome sequencing to determine the source of infection in hospitalized patients who do not have a clear infectious contact history. Recently, we encountered two seemingly separate COVID-19 clusters in a tertiary hospital. Whole viral genome sequencing distinguished the two clusters according to the viral haplotype. However, the source of infection was unclear in 14 patients with COVID-19 who were clinically unlinked to clusters #1 or #2. These patients, who had no clear history of infectious contact within the hospital (“undetermined source of infection”), had haplotypes similar to those in cluster #2 but did not have two of the mutations used to characterize cluster #2, suggesting that these 14 cases of “undetermined source of infection” were not derived from cluster #2. Whole viral genome sequencing can be useful for confirming that sporadic COVID-19 cases with an undetermined source of infection are indeed not part of clusters at the institutional level.

No abstract

Context Congenital isolated TSH deficiency (i-TSHD) is a rare form of congenital hypothyroidism. Five genes (IGSF1, IRS4, TBL1X, TRHR, and TSHB) responsible for the disease have been identified, although their relative frequencies and hypothalamic/pituitary unit phenotypes have remained to be clarified. Objectives To define the relative frequencies and hypothalamic/pituitary unit phenotypes of congenital i-TSHD resulting from single gene mutations. Patients and Methods Thirteen Japanese patients (11 boys and 2 girls) with congenital i-TSHD were enrolled. IGSF1, IRS4, TBL1X, TRHR, and TSHB were sequenced. For a TBL1X mutation (p.Asn382del), its pathogenicity was verified in vitro. For a literature review, published clinical data derived from 74 patients with congenital i-TSHD resulting from single-gene mutations were retrieved and analyzed. Results Genetic screening of the 13 study subjects revealed six mutation-carrying patients (46%), including five hemizygous IGSF1 mutation carriers and one hemizygous TBL1X mutation carrier. Among the six mutation carriers, one had intellectual disability and the other one had obesity, but the remaining four did not show nonendocrine phenotypes. Loss of function of the TBL1X mutation (p.Asn382del) was confirmed in vitro. The literature review demonstrated etiology-specific relationship between serum prolactin (PRL) levels and TRH-stimulated TSH levels with some degree of overlap. Conclusions The mutation screening study covering the five causative genes of congenital i-TSHD was performed, showing that the IGSF1 defect was the leading genetic cause of the disease. Assessing relationships between serum PRL levels and TRH-stimulated TSH levels would contribute to predict the etiologies of congenital i-TSHD.

In this study, we developed a calving prediction model based on continuous measurements of ventral tail base skin temperature (ST) with supervised machine learning and evaluated the predictive ability of the model in 2 dairy farms with distinct cattle management practices. The ST data were collected at 2or 10-min intervals from 105 and 33 pregnant cattle (mean ± standard deviation: 2.2 ± 1.8 parities) reared in farms A (freestall barn, in a temperate climate) and B (tiestall barn, in a subarctic climate), respectively. After extracting maximum hourly ST, the change in values was expressed as residual ST (rST = actual hourly ST − mean ST for the same hour on the previous 3 d) and analyzed. In both farms, rST decreased in a biphasic manner before calving. Briefly, an ambient temperature-independent gradual decrease occurred from around 36 to 16 h before calving, and an ambient temperature-dependent sharp decrease occurred from around 6 h before until calving. To make a universal calving prediction model, training data were prepared from pregnant cattle under different ambient temperatures (10 data sets were randomly selected from each of the 3 ambient temperature groups: <15°C, ≥15°C to <25°C, and ≥25°C in farm A). An hourly calving prediction model was then constructed with the training data by support vector machine based on 15 features extracted from sensing data (indicative of pre-calving rST changes) and 1 feature from non-sensor-based data (days to expected calving date). When the prediction model was applied to the data that were not part of the training process, calving within the next 24 h was predicted with sensitivities and precisions of 85.3% and 71.9% in farm A (n = 75), and 81.8% and 67.5% in farm B (n = 33), respectively. No differences were observed in means and variances of intervals from the calving alerts to actual calving between farms (12.7 ± 5.8 and 13.0 ± 5.6 h in farms A and B, respectively). Above all, a calving prediction model based on continuous measurement of ST with supervised machine learning has the potential to achieve effective calving prediction, irrespective of the rearing condition in dairy cattle.