BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

Silencing of human ferrochelatase causes abundant protoporphyrin-IX accumulation in colon cancer

Hemes and heme proteins are vital components of essentially every cell of virtually every eukaryote organism. Previously, we demonstrated accumulation of the heme precursor protoporphyrin-IX (PpIX) in gastrointestinal tumor tissues. To elucidate the mechanisms of PpIX accumulation by quantitative reverse transcriptase-polymerase chain reaction (RT-PCR), we studied expression of the relevant enzymes of the heme synthetic pathway. Here, we describe a significant down-regulation of ferrochelatase (FECH) mRNA expression in gastric, colonic, and rectal carcinomas. Accordingly, in an in vitro model of several carcinoma cell lines, ferrochelatase down-regulation and loss of enzymatic activity corresponded with an enhanced PpIX-dependent fluorescence. Direct detection of PpIX in minute amounts was achieved by a specifically developed pulsed solid-state laser dual delay fluorimetry setup. Silencing of FECH using small interfering RNA (siRNA) technology led to a maximum 50-fold increased PpIX accumulation, imageable by a specifically adapted two-photon microscopy unit. Our results show that in malignant tissue a transcriptional down-regulation of FECH occurs, which causes endogenous PpIX accumulation. Furthermore, accumulation of intracellular PpIX because of FECH siRNA silencing provides a small-molecule-based approach to molecular imaging and molecular therapyKemmner, W., Wan, K., Ruettinger, S., Ebert, B., Macdonald, R., Klamm, U., Moesta, K. T. Silencing of human ferrochelatase causes abundant protoporphyrin-IX accumulation in colon cancer

Molecular imaging of breast cancer in a transgene mouse model [Molekulare Bildgebung des Mammakarzinoms in einem transgenen Mausmodell]

Background: Purpose of the study was to detect early breast cancer and its precursors by in vivo optical molecular imaging in an animal model. Material and methods: Females of the transgene mouse strain WAP-TNP8 develop non-invasive and consequently invasive tumours of the mammary glands due to specific expression of the viral SV40 large tumour antigen induced by lactation. The molecular target for imaging was extradomain-b fibronectin (EDB-FN), which is associated with tumour angiogenesis. The optical probe was designed as a compound of an anti-EDB-FN antibody fragment and a near-infrared fluorescent dye. 30h after intravenous injection of the contrast agent, optical imaging was performed using a pulsed Laser system for excitation and an intensified CCD-camera to record fluorescence images. After optical molecular imaging all animals were sacrified and the tumours were examined histologically. Results: Initiated transgene female animals developed palpable masses of the mammary gland within 6 months (median 4 months). Imaging was performed in 5 animals with a total of 9 tumours (diameter 2–7mm, median 4mm). Applying optical molecular imaging 8 of 9 tumours were detected. The urogenital tract was contrasted unspecifically. Histological examination proved invasive epithelial tumours of the mammary gland in all cases. Conclusion: Breast cancer can be detected in vivo by near-infrared fluorescence molecular imaging targeting neoangiogenesis in a transgene mouse-model

Sparsentan in patients with IgA nephropathy: a prespecified interim analysis from a randomised, double-blind, active-controlled clinical trial

Background: Sparsentan is a novel, non-immunosuppressive, single-molecule, dual endothelin and angiotensin receptor antagonist being examined in an ongoing phase 3 trial in adults with IgA nephropathy. We report the prespecified interim analysis of the primary proteinuria efficacy endpoint, and safety. Methods: PROTECT is an international, randomised, double-blind, active-controlled study, being conducted in 134 clinical practice sites in 18 countries. The study examines sparsentan versus irbesartan in adults (aged ≥18 years) with biopsy-proven IgA nephropathy and proteinuria of 1·0 g/day or higher despite maximised renin-angiotensin system inhibitor treatment for at least 12 weeks. Participants were randomly assigned in a 1:1 ratio to receive sparsentan 400 mg once daily or irbesartan 300 mg once daily, stratified by estimated glomerular filtration rate at screening (30 to <60 mL/min per 1·73 m2 and ≥60 mL/min per 1·73 m2) and urine protein excretion at screening (≤1·75 g/day and >1·75 g/day). The primary efficacy endpoint was change from baseline to week 36 in urine protein–creatinine ratio based on a 24-h urine sample, assessed using mixed model repeated measures. Treatment-emergent adverse events (TEAEs) were safety endpoints. All endpoints were examined in all participants who received at least one dose of randomised treatment. The study is ongoing and is registered with ClinicalTrials.gov, NCT03762850. Findings: Between Dec 20, 2018, and May 26, 2021, 404 participants were randomly assigned to sparsentan (n=202) or irbesartan (n=202) and received treatment. At week 36, the geometric least squares mean percent change from baseline in urine protein–creatinine ratio was statistically significantly greater in the sparsentan group (–49·8%) than the irbesartan group (–15·1%), resulting in a between-group relative reduction of 41% (least squares mean ratio=0·59; 95% CI 0·51–0·69; p<0·0001). TEAEs with sparsentan were similar to irbesartan. There were no cases of severe oedema, heart failure, hepatotoxicity, or oedema-related discontinuations. Bodyweight changes from baseline were not different between the sparsentan and irbesartan groups. Interpretation: Once-daily treatment with sparsentan produced meaningful reduction in proteinuria compared with irbesartan in adults with IgA nephropathy. Safety of sparsentan was similar to irbesartan. Future analyses after completion of the 2-year double-blind period will show whether these beneficial effects translate into a long-term nephroprotective potential of sparsentan. Funding: Travere Therapeutics

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License