Diagnostic delay and complications for older adults with multiple myeloma

Increased attention to timely diagnosis motivated us to study 5483 patients diagnosed with multiple myeloma using Medicare claims linked to tumor registries in the Surveillance, Epidemiology and End Results programme. We calculated the time between initial visits for anemia or back pain and for myeloma diagnosis, and used logistic regression to predict the likelihood of diagnostic delay, and also the likelihood of renal or skeletal complications. The median time between sign or symptom and myeloma diagnosis was 99 days. Patients with anemia, back pain and comorbidities were more likely to experience diagnostic delay (OR 1.6, 95% CI 1.3-2.0). Diagnosis while hospitalised (OR 2.5, 95% CI 2.2-2.9) and chemotherapy treatment within 6 months of diagnosis (OR 1.4, 95% CI 1.2-1.6) significantly predicted complications; diagnostic delay did not (OR 0.9, 95% CI 0.8-1.1). Our data suggest that complications are more strongly associated with health status and myeloma severity than with diagnostic delays.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94124/1/Diagnostic delay and complications for older adults with multiple myeloma.pd

Referrals for suspected hematologic malignancy: A survey of primary care physicians

Little is known about referrals from primary care providers (PCPs) for suspected hematologic malignancies, including their clinical triggers and frequency. A random sample of 190 Massachusetts PCPs were presented with a vignette concerning a patient with a new finding of moderate anemia, asked how they would respond, and then asked what they would do if the patient returned with persistent anemia plus one additional sign or symptom. We also asked about referral behaviors for suspected hematologic malignancies during the prior year. A total of 134 (70.5%) PCPs responded. At first anemia presentation,only 3.8% reported referring to hematology. The development of a second sign or symptom yielded higher referral rates: pancytopenia 588.7%, leukopenia 5 63.9%, thrombocytopenia 5 63.9%, lymphadenopathy 5 42.9%, leukocytosis 5 37.6%, night sweats 5 25.6%, and weight loss 5 23.3%. The median yearly number (interquartile range) of patients PCPs reported suspecting of having hematologic malignancy was 5 (3, 10), and the median formally referred was 5 (3, 10). We conclude that anemia plus signs and symptoms suggestive of myelodysplasia or leukemia (compared with those suggestive of lymphoma) are more likely to prompt hematology referral. In addition, given their rarity,the numbePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93784/1/Referrals for suspected hematologic malignancy A survey of primary care physicians.pd

Delays in referral and diagnosis for chronic hematological malignancies: A literature review

To better understand the extent of diagnostic and referral delays from primary care providers (PCPs) for chronic hematologic malignancies, causes of these delays, and their possible effects on cancer outcomes, an extensive review of the literature was performed. Over 50 studies were reviewed, including many that concern delays in referral and diagnosis for solid tumors, as there was only sparse literature on delays specific to the liquid tumors. Delays for some chronic hematologic malignancies have been documented, mainly in centralized health care systems. Possible reasons for delays include PCPs' lack of exposure to hematologic malignancies, limited knowledge of associated signs and symptoms, and a reliance on patient symptoms to prompt referral (as opposed to signs and screening). Patient characteristics such as age, gender and race-ethnicity are also likely to play a role, although it is unclear if these exert their effect primarily via patient or provider mechanisms. Unfortunately, the outcomes associated with such delays are largely unreported, possibly because delay is complex to define and difficult to measure.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94128/1/Delays in referral and diagnosis for chronic hematological malignancies A literature review.pd

Brain injury-associated biomarkers of TGF-beta1, S100B, GFAP, NF-L, tTG, AbetaPP, and tau were concomitantly enhanced and the UPS was impaired during acute brain injury caused by Toxocara canis in mice

BACKGROUND: Because the outcomes and sequelae after different types of brain injury (BI) are variable and difficult to predict, investigations on whether enhanced expressions of BI-associated biomarkers (BIABs), including transforming growth factor beta1 (TGF-beta1), S100B, glial fibrillary acidic protein (GFAP), neurofilament light chain( NF-L), tissue transglutaminases (tTGs), beta-amyloid precursor proteins (AbetaPP), and tau are present as well as whether impairment of the ubiquitin-proteasome system (UPS) is present have been widely used to help delineate pathophysiological mechanisms in various BIs. Larvae of Toxocara canis can invade the brain and cause BI in humans and mice, leading to cerebral toxocariasis (CT). Because the parasitic burden is light in CT, it may be too cryptic to be detected in humans, making it difficult to clearly understand the pathogenesis of subtle BI in CT. Since the pathogenesis of murine toxocariasis is very similar to that in humans, it appears appropriate to use a murine model to investigate the pathogenesis of CT. METHODS: BIAB expressions and UPS function in the brains of mice inoculated with a single dose of 250 T. canis embryonated eggs was investigated from 3 days (dpi) to 8 weeks post- infection (wpi) by Western blotting and RT-PCR. RESULTS: Results revealed that at 4 and 8 wpi, T. canis larvae were found to have invaded areas around the choroid plexus but without eliciting leukocyte infiltration in brains of infected mice; nevertheless, astrogliosis, an indicator of BI, with 78.9~142.0-fold increases in GFAP expression was present. Meanwhile, markedly increased levels of other BIAB proteins including TGF-beta1, S100B, NF-L, tTG, AbetaPP, and tau, with increases ranging 2.0~12.0-fold were found, although their corresponding mRNA expressions were not found to be present at 8 wpi. Concomitantly, UPS impairment was evidenced by the overexpression of conjugated ubiquitin and ubiquitin in the brain. CONCLUSION: Further studies are needed to determine whether there is an increased risk of CT progression into neurodegenerative disease because neurodegeneration-associated AbetaPP and phosphorylated tau emerged in the brain. DOI: 10.1186/1471-2334-8-8

Measurement of neutron-proton capture in the SNO+ water phase

The SNO+ experiment collected data as a low-threshold water Cherenkov detector from September 2017 to July 2019. Measurements of the 2.2-MeV $\gamma$ produced by neutron capture on hydrogen have been made using an Am-Be calibration source, for which a large fraction of emitted neutrons are produced simultaneously with a 4.4-MeV $\gamma$. Analysis of the delayed coincidence between the 4.4-MeV $\gamma$ and the 2.2-MeV capture $\gamma$ revealed a neutron detection efficiency that is centered around 50% and varies at the level of 1% across the inner region of the detector, which to our knowledge is the highest efficiency achieved among pure water Cherenkov detectors. In addition, the neutron capture time constant was measured and converted to a thermal neutron-proton capture cross section of $336.3^{+1.2}_{-1.5}$ mb

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Development, characterisation, and deployment of the SNO+ liquid scintillator

A liquid scintillator consisting of linear alkylbenzene as the solvent and 2,5-diphenyloxazole as the fluor was developed for the SNO+ experiment. This mixture was chosen as it is compatible with acrylic and has a competitive light yield to pre-existing liquid scintillators while conferring other advantages including longer attenuation lengths, superior safety characteristics, chemical simplicity, ease of handling, and logistical availability. Its properties have been extensively characterized and are presented here. This liquid scintillator is now used in several neutrino physics experiments in addition to SNO+

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease