Host country nationals to the rescue: a social categorization approach to expatriate adjustment

The present study proposes a significant role for host country nationals (HCNs) in the expatriate adjustment process. Based on self-categorizaton theory, newcomer socialization research, organizational citizenship behavior (OCB) research, and models of expatriate adjustment, I present a model proposing how social categorization processes influence HCNs' willingness to engage in adjustment-facilitating organizational citizenship behaviors (AOCBs). I further propose that these behaviors have a significant impact on expatriates' adjustment and in turn, other important job-related outcomes of the expatriate. Hypotheses were tested on 115 expatriates and 53 HCNs. Expatriates were contacted directly or via an organizational contact. HCNs were either contacted directly or nominated by their expatriate counterpart to participate in the study. Results reveal support for the main tenets of the model. The willingness to engage in AOCBs was related to outgroup categorization, collectivism, and perceptions of justice. Social support provided by HCNs was found to significantly relate to HCNs' perceptions of their expatriate co-worker's adjustment. Expatriates, however, indicated that spousal adjustment and language ability were more important for their own adjustment. Adjustment was related to other key expatriate outcomes. The research and managerial implications of these results are discussed

When do host country nationals help expatriates? The roles of identification with the multinational enterprise and career development support by the subsidiary

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Fluctuation of BCR-ABL1 qPCRIS level beyond 0.1%IS after stopping tyrosine kinase inhibitor in chronic myeloid leukaemia patients with deep molecular response for at least two years

Fluctuation of BCR-ABL1 real-time quantitative polymerase chain reaction in International Scale (qPCRIS ) level below major molecular response (MMR) (0.1%IS ) is a known phenomenon after stopping tyrosine kinase inhibitor (TKI) in chronic myeloid leukaemia (CML) patients who are attempting treatment free remission (TFR). We report here four cases of fluctuation beyond MMR during conduct of a Malaysia Stop TKI Trial (MSIT) to examine the validity of the commonly used relapse criterion – loss of MMR for one reading – aiming to provide evidence in setting relapse criteria for future CML patients who want to attempt TFR

A randomized control trial comparing peginterferon-α-2a versus observation after stopping tyrosine kinase inhibitor in chronic myeloid leukemia with deep molecular response for at least two years

Background: There is much advancement in treatment of chronic myeloid leukemia (CML) since the approval of first tyrosine kinase inhibitor (TKI), imatinib, by US FDA in 2001. One of them is definitely the concept of stopping TKI, starting at the CML patients who have achieved deep molecular response (MR) of MR4.5 for a reasonable long period of at least two years, pioneered by the researchers from French and Australia. Meanwhile, interferon, the standard treatment of CML before the era of TKI, showed that interferon-responded patients may indeed retain the response once interferon was withdrawn via interferon-induced immunity towards the leukemic clone. This is further supported by stop trial in Japan, in which after stopping TKI, interferon was given for 2 years and it showed a higher drug-free rate compared to stop trial from French and Australia. Hence, it is logical to postulate the use of interferon after TKI was stopped when patients have attained deep MR for more than two years will increase the percentage of patients remain TKI-free on follow-up. Materials and Methods: This is a multi-center randomized controlled trial. Adult CML patients, diagnosed in chronic phase, treated with ongoing TKI for at least 3 years without previous history of TKI failure and have achieved stable deep MR on International Scale for ≥2 years with at least 2 readings of MR4.5 (including the latest before study entry), were randomized into 2 arms: (1) peginterferon-α-2a, subcutaneous weekly, starting at 180 mcg, or (2) observation. Disease is monitored by PCR (centralized in Ampang Hospital) at monthly for the first year, 2 monthly for the second year and 3 monthly for the third year. Relapse is defined as either (i) one reading of loss of major MR, i.e. reading of >0.1% IS and confirmed by second analysis taken 1 month later if the first analysis point reading is ≤1% IS, or (ii) positivity of BCR-ABL1 transcripts, as confirmed by a second analysis point, indicating the increase (at least 1 log) in relation to the first analysis point at two successive assessments. Quality of Life is assessed using EORTC QLO-C30. Results: At the time of writing, total of 8 patients were randomized, 5 into peginterferon arm, 3 into observation arm, all were on imatinib, M:F = 4:4, Malay: Chinese:Indian = 3:4:1, median age 49.5 (range 25-58), median follow-up 4 months (range 1-6) and none of them relapse. Two patients developed imatinib withdrawal syndrome, both female on observation arm, one was mild and resolved after 2 months but one was severe and needed termination after 2 months and restarted on imatinib. Two patients in peginterferon arm developed mild hepatitis with liver enzymes <2x of ULN. Four patients were able to tolerate peginterferon-α-2a at the dose of 180 mcg weekly, while one patient needed dose reduction to 90 mcg weekly. Quality of life score comparing two months after stopping TKI and baseline will be presented in the conference later. Conclusion: No conclusive date can be drawn so far because sample size is small and follow-up is short. Nonetheless, this trial provides Malaysian CML a platform to stop TKI safely

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery