Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging

Purpose To compare two artificial intelligence software packages performing normative brain volumetry and explore whether they could differently impact dementia diagnostics in a clinical context. Methods Sixty patients (20 Alzheimer’s disease, 20 frontotemporal dementia, 20 mild cognitive impairment) and 20 controls were included retrospectively. One MRI per subject was processed by software packages from two proprietary manufacturers, producing two quantitative reports per subject. Two neuroradiologists assigned forced-choice diagnoses using only the normative volumetry data in these reports. They classified the volumetric profile as “normal,” or “abnormal”, and if “abnormal,” they specified the most likely dementia subtype. Differences between the packages’ clinical impact were assessed by comparing (1) agreement between diagnoses based on software output; (2) diagnostic accuracy, sensitivity, and specificity; and (3) diagnostic confidence. Quantitative outputs were also compared to provide context to any diagnostic differences. Results Diagnostic agreement between packages was moderate, for distinguishing normal and abnormal volumetry (K = .41–.43) and for specific diagnoses (K = .36–.38). However, each package yielded high inter-observer agreement when distinguishing normal and abnormal profiles (K = .73–.82). Accuracy, sensitivity, and specificity were not different between packages. Diagnostic confidence was different between packages for one rater. Whole brain intracranial volume output differed between software packages (10.73%, p < .001), and normative regional data interpreted for diagnosis correlated weakly to moderately (rs = .12–.80). Conclusion Different artificial intelligence software packages for quantitative normative assessment of brain MRI can produce distinct effects at the level of clinical interpretation. Clinics should not assume that different packages are interchangeable, thus recommending internal evaluation of packages before adoption.

The number and composition of the main groups of microorganisms in alluvial drained soils, depending on the type of land, the use of fertilizers and biological products

The paper considers the results of studying the number and composition of the microbiological community of alluvial bog soils formed in the floodplain of the Kudma River, Bogorodsky District, Nizhny Novgorod Region. The microbiological pool of the soil was estimated on the basis of counting the total number of colonies on nutrient media: MPA, KAA, Gauze No. 1, Chapeka-Doksa, Golodny agar. Studies have established that the largest microbial pool is possessed by the soils of an 18-year old fallow, the lowest - by the soils of an undrained area; on the plots used for haymaking and arable land, the total number of microorganisms was 1.5 times lower than in the fallow. The most common in the studied soils were representatives of the heterotroph group (MPA medium). The composition of microorganisms was determined by subtype differences between soils, the degree of hydromorphism, and the type of land. According to the results of the vegetation experiment, it was found that the number and composition of the soil microbiota depended to a greater extent on the temperature regime and biological products; the greatest positive effect was produced by preparations containing strains of nitrogen-fixing microorganisms. Keywords: ALLUVIAL SWEET DRAINED SOILS, BEDROOM, HAYOFET, GRASSLAND, ACTINOMYCETES, BACTERIA, MUSHROOM MICROORGANISMS, FERTILIZERS, BIOPREPARATIONS

Overexpression of Replication-Dependent Histone Signifies a Subset of Dedifferentiated Liposarcoma with Increased Aggressiveness

Liposarcoma (LPS) is an adult soft tissue malignancy that arises from fat tissue, where well-differentiated (WD) and dedifferentiated (DD) forms are the most common. DDLPS represents the progression of WDLPS into a more aggressive high-grade and metastatic form. Although a few DNA copy-number amplifications are known to be specifically found in WD- or DDLPS, systematic genetic differences that signify subtype determination between WDLPS and DDLPS remain unclear. Here, we profiled the genome and transcriptome of 38 LPS tumors to uncover the genetic signatures of subtype differences. Replication-dependent histone (RD-HIST) mRNAs were highly elevated and their regulation was disrupted in a subset of DDLPS, increasing cellular histone molecule levels, as measured using RNA-seq (the averaged fold change of 53 RD-HIST genes between the DD and WD samples was 10.9) and immunohistochemistry. The change was not observed in normal tissues. Integrated whole-exome sequencing, RNA-seq, and methylation analyses revealed that the overexpressed HMGA2 (the fold change between DD and WD samples was 7.3) was responsible for the increased RD-HIST level, leading to aberrant cell proliferation. Therefore, HMGA2-mediated elevation of RD-HISTs were crucial events in determining the aggressiveness of DDLPS, which may serve as a biomarker for prognosis prediction for liposarcoma patients.

Gonadotroph Tumors Show Subtype Differences that Might Have Implications for Therapy

Gonadotroph tumors, although frequent, are poorly studied and understood, being usually included in the larger nonfunctioning pituitary neuroendocrine tumors (PitNETs) group. Moreover, in comparison to the other types of PitNETs, no established medical treatment is currently available for gonadotroph tumors. Here, we performed a retrospective study and analyzed the clinicopathological characteristics of 98 gonadotroph tumors operated in a single large pituitary center. Although being larger in men (p = 0.01), the aggressiveness of gonadotroph tumors did not appear to be sex-related. LH tumors were rare (4/98) and exclusively encountered in men. Somatostatin receptor type 5 (SST5) was absent in all analyzed tumors. The immunoreactive score (IRS) of somatostatin receptor type 2 (SST2) and of estrogen receptor alpha (ERα) was associated with the histological subtype (p = 0.01 and p = 0.02). IRS ERα correlated moderately with IRS SST2 in all (rho = 0.44, adjusted p-value = 0.0001) and in male (rho = 0.51, adjusted p-value = 0.0002) patients, and with follicle-stimulating hormone (FSH) percentage in all (rho = 0.40, adjusted p-value = 0.0005) and in female (rho = 0.58, adjusted p-value = 0.004) patients. In conclusion, gonadotroph tumors exhibit histological characteristics pinpointing the existence of several subtypes. Their heterogeneity warrants further investigations and may have to be taken into account when studying these tumors and investigating treatment options.

Biological subtypes of Alzheimer disease

ObjectiveTo test the hypothesis that distinct subtypes of Alzheimer disease (AD) exist and underlie the heterogeneity within AD, we conducted a systematic review and meta-analysis on AD subtype studies based on postmortem and neuroimaging data.MethodsEMBASE, PubMed, and Web of Science databases were consulted until July 2019.ResultsNeuropathology and neuroimaging studies have consistently identified 3 subtypes of AD based on the distribution of tau-related pathology and regional brain atrophy: typical, limbic-predominant, and hippocampal-sparing AD. A fourth subtype, minimal atrophy AD, has been identified in several neuroimaging studies. Typical AD displays tau-related pathology and atrophy both in hippocampus and association cortex and has a pooled frequency of 55%. Limbic-predominant, hippocampal-sparing, and minimal atrophy AD had a pooled frequency of 21%, 17%, and 15%, respectively. Between-subtype differences were found in age at onset, age at assessment, sex distribution, years of education, global cognitive status, disease duration, APOE ε4 genotype, and CSF biomarker levels.ConclusionWe identified 2 core dimensions of heterogeneity: typicality and severity. We propose that these 2 dimensions determine individuals' belonging to one of the AD subtypes based on the combination of protective factors, risk factors, and concomitant non-AD brain pathologies. This model is envisioned to aid with framing hypotheses, study design, interpretation of results, and understanding mechanisms in future subtype studies. Our model can be used along the A/T/N classification scheme for AD biomarkers. Unraveling the heterogeneity within AD is critical for implementing precision medicine approaches and for ultimately developing successful disease-modifying drugs for AD.