Digital droplet PCR is a specific and sensitive tool for detecting IDH2 mutations in acute myeloid leukemia patients

Isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) interfere with cellular metabolism contributing to oncogenesis. Mutations of IDH2 at R140 and R172 residues are observed in 20% of acute myeloid leukemias (AML), and the availability of the IDH2 inhibitor Enasidenib made IDH2 mutational screening a clinical need. The aim of this study was to set a new quantitative polymerase chain reaction (PCR) technique, the drop-off digital droplet PCR (drop-off ddPCR), as a sensitive and accurate tool for detecting IDH2 mutations. With this technique we tested 60 AML patients. Sanger sequencing identified 8/60 (13.5%) mutated cases, while ddPCR and the amplification refractory mutation system (ARMS) PCR, used as a reference technique, identified mutations in 13/60 (21.6%) cases. When the outcome of IDH2-mutated was compared to that of wild-type patients, no significant difference in terms of quality of response, overall survival, or progression-free survival was observed. Finally, we monitored IDH2 mutations during follow-up in nine cases, finding that IDH2 can be considered a valid marker of minimal residual disease (MRD) in 2/3 of our patients. In conclusion, a rapid screening of IDH2 mutations is now a clinical need well satisfied by ddPCR, but the role of IDH2 as a marker for MRD still remains a matter of debate

The assessment of minimal residual disease versus that of somatic mutations for predicting the outcome of acute myeloid leukemia patients

Background: In addition to morphological and cytogenetic features, acute myeloid leukemias are characterized by mutations that can be used for target-therapy; also the minimal/measurable residual disease (MRD) could be an important prognostic factor. The purpose of this retrospective study was to investigate if somatic mutations could represent an additional prognostic value in respect of MRD alone. Method: At baseline, 98 patients were tested for NPM1, FLT3, and for WT1 expression; 31 for ASXL1, TET2, IDH1, IDH2, N-RAS, WT1, c-KIT, RUNX1, and DNMT3A. The same genes have been also tested after induction and consolidation. Results: Overall, 60.2% of our patients resulted mutated: 24.5% carried mutations of FLT3-ITD, 38.7% of NPM1, 48.4% of c-KIT, 25.8% of N-RAS and 19.3% of IDH2. The probability of achieving a complete response (CR) was higher for younger patients, with low ELN risk score, NPM1-mutated, with low WT1 levels, and without FLT3. The presence of additional mutations represented a poor predictive factor: only 19% of these cases achieved CR in comparison to 43% of subjects without any of it. Concerning survival, it was conditioned by a lower ELN risk score, younger age, reduction > 1 log of the NPM1 mutational burden, disappearance of FLT3 mutations and lower WT1 expression. Regarding the role of the additional mutations, they impaired the outcome of 20% of the already MRD-negative patients. Concerning the possibility of predicting relapse, we observed an increase of the NPM1 mutational burden at the time-point immediately preceding the relapse (about 2 months earlier) in 50% of subjects. Similarly concerning WT1, an increase of its expression anticipated disease recurrence in 64% of cases. Conclusions: We demonstrated that additional somatic mutations are able to impair outcome of the already MRD-negative subjects. About MRD, we suggest a prognostic role also for the WT1 expression. Finally, we considered as relevant the assessment of NPM1 quantity clearance instead of the presence/absence of mutations alone. Still remains in doubt the utility in terms of long-term prognosis of a baseline more complex mutational screening; we could hypothesize that it would be useful for those patients where other markers are not available or who reached the MRD negativity

The hOCT1 and ABCB1 polymorphisms do not influence the pharmacodynamics of nilotinib in chronic myeloid leukemia

First-line nilotinib in chronic myeloid leukemia is more effective than imatinib to achieve early and deep molecular responses, despite poor tolerability or failure observed in one-third of patients. The toxicity and efficacy of tyrosine kinase inhibitors might depend on the activity of transmembrane transporters. However, the impact of transporters genes polymorphisms in nilotinib setting is still debated. We investigated the possible correlation between single nucleotide polymorphisms of hOCT1 (rs683369 [c.480C > G]) and ABCB1 (rs1128503 [c.1236C > T], rs2032582 [c.2677G > T/A], rs1045642 [c.3435C > T]) and nilotinib efficacy and toxicity in a cohort of 78 patients affected by chronic myeloid leukemia in the context of current clinical practice. The early molecular response was achieved by 81% of patients while 64% of them attained deep molecular response (median time, 26 months). The 36-month event-free survival was 86%, whereas 58% of patients experienced toxicities. Interestingly, hOCT1 and ABCB1 polymorphisms alone or in combination did not influence event-free survival or the adverse events rate. Therefore, in contrast to data obtained in patients treated with imatinib, hOCT1 and ABCB1 polymorphisms do not impact on nilotinib efficacy or toxicity. This could be relevant in the choice of the first-line therapy: patients with polymorphisms that negatively condition imatinib efficacy might thus receive nilotinib as first-line therapy

Infected pancreatic necrosis: outcomes and clinical predictors of mortality. A post hoc analysis of the MANCTRA-1 international study

: The identification of high-risk patients in the early stages of infected pancreatic necrosis (IPN) is critical, because it could help the clinicians to adopt more effective management strategies. We conducted a post hoc analysis of the MANCTRA-1 international study to assess the association between clinical risk factors and mortality among adult patients with IPN. Univariable and multivariable logistic regression models were used to identify prognostic factors of mortality. We identified 247 consecutive patients with IPN hospitalised between January 2019 and December 2020. History of uncontrolled arterial hypertension (p = 0.032; 95% CI 1.135-15.882; aOR 4.245), qSOFA (p = 0.005; 95% CI 1.359-5.879; aOR 2.828), renal failure (p = 0.022; 95% CI 1.138-5.442; aOR 2.489), and haemodynamic failure (p = 0.018; 95% CI 1.184-5.978; aOR 2.661), were identified as independent predictors of mortality in IPN patients. Cholangitis (p = 0.003; 95% CI 1.598-9.930; aOR 3.983), abdominal compartment syndrome (p = 0.032; 95% CI 1.090-6.967; aOR 2.735), and gastrointestinal/intra-abdominal bleeding (p = 0.009; 95% CI 1.286-5.712; aOR 2.710) were independently associated with the risk of mortality. Upfront open surgical necrosectomy was strongly associated with the risk of mortality (p < 0.001; 95% CI 1.912-7.442; aOR 3.772), whereas endoscopic drainage of pancreatic necrosis (p = 0.018; 95% CI 0.138-0.834; aOR 0.339) and enteral nutrition (p = 0.003; 95% CI 0.143-0.716; aOR 0.320) were found as protective factors. Organ failure, acute cholangitis, and upfront open surgical necrosectomy were the most significant predictors of mortality. Our study confirmed that, even in a subgroup of particularly ill patients such as those with IPN, upfront open surgery should be avoided as much as possible. Study protocol registered in ClinicalTrials.Gov (I.D. Number NCT04747990)

Learning transferable policies for autonomous planetary landing via deep reinforcement learning

The aim of this work is to develop an application for autonomous landing, exploiting the properties of Deep Reinforcement Learning and Transfer Learning in order to tackle the problem of planetary landing on unknown or barely-known extra-terrestrial environments by learning good-performing policies, which are transferable from the training environment to other, new environments, without losing optimality. To this end, we model a real-physics simulator, by means of the Bullet/PyBullet library, composed by a lander, defined through the standard ROS/URDF framework and realistic 3D terrain models, for which we adapt official NASA 3D meshes, reconstructed from the data retrieved during missions. Where such models are not available, we reconstruct the terrain from mission imagery-generally SAR imagery. In this setup, we train a Deep Reinforcement Learning model-using DDPG and SAC, then comparing the outcomes-to autonomously land on the lunar environment. Moreover, we perform transfer learning on Mars and Titan environments. While still preliminary, our results show that DDPG and SAC can learn good landing policies, that can be transferred to other environments. Good policies can be learned by the SAC algorithm also in the case of atmospheric disturbances-e.g. gusts

7'-Aminonaphthazarin antibiotic derivatives

The title compds. I (R = H, OH; R1 = H, (un)substituted C1-4 alkyl), II (Y = C1-5 alkylmercapto, (un)substituted PhS, C1-5 alkylsulfinyl, (un)substituted phenylsulfinyl, etc.), and III (X = Cl, Br), which demonstrate antibiotic activity against gram pos. and neg. microorganisms, fungi, and protozoa, and which are suitable for the treatment of vaginal infections, are prepd. and pharmaceutical formulations contg. them presented. Thus, 7'-(ethylamino)purpuromycin, prepd. in 6 steps from purpuromycin, demonstrated min. inhibitory concn. of 8 \u3bcg/mL against Trichomonas vaginalis and 2\u3bcg/mL against Gardnerella vaginalus (ATCC 14018)

Preparation of new substituted alkylamide derivatives of teicoplanin as antibacterials

The title compds. [I; R = H, protecting group; Y = NR1X1(XX2)p(TX3)qW; R1 = H, alkyl; T, X = O, (substituted) imino; X1, X2, X3 = C2-10 alkylene; W = OH, amino; p = 1-50; q = 0-12; A = H, N-acylated \u3b2-D-2-deoxy-2-aminoglucopyranosyl; B = H, N-acetyl-\u3b2-D-2-deoxy-2-aminoglucopyranosyl; M = H, \u3b1-D-mannopyranosyl; B = H only when both A, M = H], were prepd. Thus, teicoplanin A1 component 2 in ET3N/DMF was treated with PhCH2O2CCl in acetone to give 3c96% of the N-15 CBZ deriv. This was esterified with ClCH2CN in DMF/Et3N in 3c98% yield and the ester was treated with H2N(CH2)2NH(CH2)2NH2 in DMF followed by hydrogenolysis to give I [A = N-(8-methylnonanoyl)-\u3b2-D-2-deoxy-2-aminoglucopyranosyl, B = N-acetyl-\u3b2-D-2-deoxy-2-aminoglucopyranosyl, M = \u3b1-D-mannopyranosyl, Y = H2NCH2CH2NHCH2CH2NH, R = H] (II). II had an ED50 of 0.09 mg/kg s.c. against Streptomyces pyrogenes C203 in mice. Several I were active against multi-resistant Pseudomonas aeruginosa with MIC of 4-128 \u3bcg/mL